Loss of 'small-world' networks in Alzheimer's disease: graph analysis of FMRI resting-state functional connectivity

Background

Local network connectivity disruptions in Alzheimer''s disease patients have been found using graph analysis in BOLD fMRI. Other studies using MEG and cortical thickness measures, however, show more global long distance connectivity changes, both in functional and structural imaging data. The form and role of functional connectivity changes thus remains ambiguous. The current study shows more conclusive data on connectivity changes in early AD using graph analysis on resting-state condition fMRI data.

Methodology/Principal Findings

18 mild AD patients and 21 healthy age-matched control subjects without memory complaints were investigated in resting-state condition with MRI at 1.5 Tesla. Functional coupling between brain regions was calculated on the basis of pair-wise synchronizations between regional time-series. Local (cluster coefficient) and global (path length) network measures were quantitatively defined. Compared to controls, the characteristic path length of AD functional networks is closer to the theoretical values of random networks, while no significant differences were found in cluster coefficient. The whole-brain average synchronization does not differ between Alzheimer and healthy control groups. Post-hoc analysis of the regional synchronization reveals increased AD synchronization involving the frontal cortices and generalized decreases located at the parietal and occipital regions. This effectively translates in a global reduction of functional long-distance links between frontal and caudal brain regions.

Conclusions/Significance

We present evidence of AD-induced changes in global brain functional connectivity specifically affecting long-distance connectivity. This finding is highly relevant for it supports the anterior-posterior disconnection theory and its role in AD. Our results can be interpreted as reflecting the randomization of the brain functional networks in AD, further suggesting a loss of global information integration in disease.     

Imaging of functional connectivity in the mouse brain

Functional neuroimaging (e.g., with fMRI) has been difficult to perform in mice, making it challenging to translate between human fMRI studies and molecular and genetic mechanisms. A method to easily perform large-scale functional neuroimaging in mice would enable the discovery of functional correlates of genetic manipulations and bridge with mouse models of disease. To satisfy this need, we combined resting-state functional connectivity mapping with optical intrinsic signal imaging (fcOIS). We demonstrate functional connectivity in mice through highly detailed fcOIS mapping of resting-state networks across most of the cerebral cortex. Synthesis of multiple network connectivity patterns through iterative parcellation and clustering provides a comprehensive map of the functional neuroarchitecture and demonstrates identification of the major functional regions of the mouse cerebral cortex. The method relies on simple and relatively inexpensive camera-based equipment, does not require exogenous contrast agents and involves only reflection of the scalp (the skull remains intact) making it minimally invasive. In principle, fcOIS allows new paradigms linking human neuroscience with the power of molecular/genetic manipulations in mouse models.     

Abnormal brain default-mode network functional connectivity in drug addicts

Background

The default mode network (DMN) is a set of brain regions that exhibit synchronized low frequency oscillations at resting-state, and is believed to be relevant to attention and self-monitoring. As the anterior cingulate cortex and hippocampus are impaired in drug addiction and meanwhile are parts of the DMN, the present study examined addiction-related alteration of functional connectivity of the DMN.

Methodology

Resting-state functional magnetic resonance imaging data of chronic heroin users (14 males, age: 30.1±5.3 years, range from 22 to 39 years) and non-addicted controls (13 males, age: 29.8±7.2 years, range from 20 to 39 years) were investigated with independent component analysis to address their functional connectivity of the DMN.

Principal Findings

Compared with controls, heroin users showed increased functional connectivity in right hippocampus and decreased functional connectivity in right dorsal anterior cingulate cortex and left caudate in the DMN.

Conclusions

These findings suggest drug addicts'' abnormal functional organization of the DMN, and are discussed as addiction-related abnormally increased memory processing but diminished cognitive control related to attention and self-monitoring, which may underlie the hypersensitivity toward drug related cues but weakened strength of cognitive control in the state of addiction.     

Estimating brain functional connectivity with sparse multivariate autoregression

There is much current interest in identifying the anatomical and functional circuits that are the basis of the brain's computations, with hope that functional neuroimaging techniques will allow the in vivo study of these neural processes through the statistical analysis of the time-series they produce. Ideally, the use of techniques such as multivariate autoregressive (MAR) modelling should allow the identification of effective connectivity by combining graphical modelling methods with the concept of Granger causality. Unfortunately, current time-series methods perform well only for the case that the length of the time-series Nt is much larger than p, the number of brain sites studied, which is exactly the reverse of the situation in neuroimaging for which relatively short time-series are measured over thousands of voxels. Methods are introduced for dealing with this situation by using sparse MAR models. These can be estimated in a two-stage process involving (i) penalized regression and (ii) pruning of unlikely connections by means of the local false discovery rate developed by Efron. Extensive simulations were performed with idealized cortical networks having small world topologies and stable dynamics. These show that the detection efficiency of connections of the proposed procedure is quite high. Application of the method to real data was illustrated by the identification of neural circuitry related to emotional processing as measured by BOLD.     

Undirected graphs of frequency-dependent functional connectivity in whole brain networks

We explored properties of whole brain networks based on multivariate spectral analysis of human functional magnetic resonance imaging (fMRI) time-series measured in 90 cortical and subcortical subregions in each of five healthy volunteers studied in the (no-task) resting state. We note that undirected graphs representing conditional independence between multivariate time-series can be more readily approached in the frequency domain than the time domain. Estimators of partial coherency and normalized partial mutual information phi, an integrated measure of partial coherence over an arbitrary frequency band, are applied. Using these tools, we replicate the prior observations that bilaterally homologous brain regions tend to be strongly connected and functional connectivity is generally greater at low frequencies [0.0004, 0.1518 Hz]. We also show that long-distance intrahemispheric connections between regions of prefrontal and parietal cortex were more salient at low frequencies than at frequencies greater than 0.3 Hz, whereas many local or short-distance connections, such as those comprising segregated dorsal and ventral paths in posterior cortex, were also represented in the graph of high-frequency connectivity. We conclude that the partial coherency spectrum between a pair of human brain regional fMRI time-series depends on the anatomical distance between regions: long-distance (greater than 7 cm) edges represent conditional dependence between bilaterally symmetric neocortical regions, and between regions of prefrontal and parietal association cortex in the same hemisphere, are predominantly subtended by low-frequency components.     

Dynamic changes in brain functional connectivity during concurrent dual-task performance

This study investigated the spatial, spectral, temporal and functional proprieties of functional brain connections involved in the concurrent execution of unrelated visual perception and working memory tasks. Electroencephalography data was analysed using a novel data-driven approach assessing source coherence at the whole-brain level. Three connections in the beta-band (18-24 Hz) and one in the gamma-band (30-40 Hz) were modulated by dual-task performance. Beta-coherence increased within two dorsofrontal-occipital connections in dual-task conditions compared to the single-task condition, with the highest coherence seen during low working memory load trials. In contrast, beta-coherence in a prefrontal-occipital functional connection and gamma-coherence in an inferior frontal-occipitoparietal connection was not affected by the addition of the second task and only showed elevated coherence under high working memory load. Analysis of coherence as a function of time suggested that the dorsofrontal-occipital beta-connections were relevant to working memory maintenance, while the prefrontal-occipital beta-connection and the inferior frontal-occipitoparietal gamma-connection were involved in top-down control of concurrent visual processing. The fact that increased coherence in the gamma-connection, from low to high working memory load, was negatively correlated with faster reaction time on the perception task supports this interpretation. Together, these results demonstrate that dual-task demands trigger non-linear changes in functional interactions between frontal-executive and occipitoparietal-perceptual cortices.     

Distance-based analysis of variance for brain connectivity

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures, including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that might mask the salient features of high-dimensional distributions. In this work, we consider a general framework for two-sample testing of complex structures by studying generalized within-group and between-group variances based on distances between complex and potentially high-dimensional observations. We derive an asymptotic approximation to the null distribution of the ANOVA test statistic, and conduct simulation studies with scalar and graph outcomes to study finite sample properties of the test. Finally, we apply our test to our motivating study of structural connectivity in autism spectrum disorder.     

Plasma biomarkers of brain atrophy in Alzheimer's disease

Peripheral biomarkers of Alzheimer''s disease (AD) reflecting early neuropathological change are critical to the development of treatments for this condition. The most widely used indicator of AD pathology in life at present is neuroimaging evidence of brain atrophy. We therefore performed a proteomic analysis of plasma to derive biomarkers associated with brain atrophy in AD. Using gel based proteomics we previously identified seven plasma proteins that were significantly associated with hippocampal volume in a combined cohort of subjects with AD (N = 27) and MCI (N = 17). In the current report, we validated this finding in a large independent cohort of AD (N = 79), MCI (N = 88) and control (N = 95) subjects using alternative complementary methods—quantitative immunoassays for protein concentrations and estimation of pathology by whole brain volume. We confirmed that plasma concentrations of five proteins, together with age and sex, explained more than 35% of variance in whole brain volume in AD patients. These proteins are complement components C3 and C3a, complement factor-I, γ-fibrinogen and alpha-1-microglobulin. Our findings suggest that these plasma proteins are strong predictors of in vivo AD pathology. Moreover, these proteins are involved in complement activation and coagulation, providing further evidence for an intrinsic role of these pathways in AD pathogenesis.     

Functional connectivity fMRI of the rodent brain: comparison of functional connectivity networks in rat and mouse

At present, resting state functional MRI (rsfMRI) is increasingly used in human neuropathological research. The present study aims at implementing rsfMRI in mice, a species that holds the widest variety of neurological disease models. Moreover, by acquiring rsfMRI data with a comparable protocol for anesthesia, scanning and analysis, in both rats and mice we were able to compare findings obtained in both species. The outcome of rsfMRI is different for rats and mice and depends strongly on the applied number of components in the Independent Component Analysis (ICA). The most important difference was the appearance of unilateral cortical components for the mouse resting state data compared to bilateral rat cortical networks. Furthermore, a higher number of components was needed for the ICA analysis to separate different cortical regions in mice as compared to rats.     

阿尔采末病与脑缺血的相互作用

最近的观点认为脑缺血与阿尔采末病(Alzheimer’sdisease,AD)之间存在密切联系。脑缺血可能与AD的发病机制有关,而严重记忆下降以及潜在AD患者发生脑缺血的机率增加。阿朴脂蛋白E(ApoE)的等位基因是AD的风险因素,同时也是与脑缺血和出血愈后有关的风险因素。在中枢神经系统中AD神经病理学和缺血损伤的发展均存在胶质细胞的活化和炎性介质的上调。已证实,抗炎药物对于AD和缺血损伤具有防治作用。     

Lipid peroxidation in aging brain and Alzheimer's disease

Lipid peroxidation is one of the major outcomes of free radical-mediated injury that directly damages membranes and generates a number of secondary products, both from fission and endocyclization of oxygenated fatty acids that possess neurotoxic activity. Numerous studies have demonstrated increased lipid peroxidation in brain of patients with Alzheimer's disease (AD) compared with age-matched controls. These data include quantification of fission and endocyclized products such as 4-hydroxy-2-nonenal, acrolein, isoprostanes, and neuroprostanes. Immunohistochemical and biochemical studies have localized the majority of lipid peroxidation products to neurons. A few studies have consistently demonstrated increased cerebrospinal fluid (CSF) levels of isoprostanes in AD patients early in the course of their dementia, and one study has suggested that CSF isoprostanes may improve the laboratory diagnostic accuracy for AD. Similar analyses of control individuals over a wide range of ages indicate that brain lipid peroxidation is not a significant feature of usual aging. Quantification of isoprostanes in plasma and urine of AD patients has yielded inconsistent results. These results indicate that brain lipid peroxidation is a potential therapeutic target in probable AD patients, and that CSF isoprostanes may aid in the assessment of antioxidant experimental therapeutics and the laboratory diagnosis of AD.     

Phospholipase D in brain function and Alzheimer's disease

Alzheimer's disease is the most common neurodegenerative disorder. Although lipids are major constituents of brain, their role in Alzheimer's disease pathogenesis is poorly understood. Much attention has been given to cholesterol, but growing evidence suggests that other lipids, such as phospholipids, might play an important role in this disorder. In this review, we will summarize the evidence linking phospholipase D, a phosphatidic acid-synthesizing enzyme, to multiple aspects of normal brain function and to Alzheimer's disease. The role of phospholipase D in signaling mechanisms downstream of beta-amyloid as well as in the trafficking and processing of amyloid precursor protein will be emphasized.     

Proteomic identification of nitrated proteins in Alzheimer's disease brain

Nitration of tyrosine in biological conditions represents a pathological event that is associated with several neurodegenerative diseases, such as amyotrophic lateral sclerosis, Parkinson's disease and Alzheimer's disease (AD). Increased levels of nitrated proteins have been reported in AD brain and CSF, demonstrating the potential involvement of reactive nitrogen species (RNS) in neurodegeneration associated with this disease. Reaction of NO with O2- leads to formation of peroxynitrite ONOO-, which following protonation, generates cytotoxic species that oxidize and nitrate proteins. Several findings suggest an important role of protein nitration in modulating the activity of key enzymes in neurodegenerative disorders, although extensive studies on specific targets of protein nitration in disease are still missing. The present investigation represents a further step in understanding the relationship between oxidative modification of protein and neuronal death in AD. We previously applied a proteomics approach to determine specific targets of protein oxidation in AD brain, by successfully coupling immunochemical detection of protein carbonyls with two-dimensional polyacrylamide gel electrophoresis and mass spectrometry analysis. In the present study, we extend our investigation of protein oxidative modification in AD brain to targets of protein nitration. The identification of six targets of protein nitration in AD brain provides evidence to the importance of oxidative stress in the progression of this dementing disease and potentially establishes a link between RNS-related protein modification and neurodegeneration.     

Activity of phosphatidylethanolamine-N-methyltransferase in brain affected by Alzheimer's disease

To determine whether phospholipid abnormality in Alzheimer's disease is associated with modification of phosphatidylethanolamine-N-methyltransferase, the activity of the enzyme was analysed in the frontal and occipital cortex of the brain from patients with Alzheimer's disease and from aged-matched control. The optimum pH for phosphatidylethanolamine-N-methyltransferase in human brain was 9.0. The enzyme activity was stimulated by detergent TWEEN 20 but inhibited by Triton X-100. Neither magnesium dependence nor chemical methylation was found. A decrease in activity of phosphatidylethanolamine-N-methyltransferase was observed in the frontal cortex of brain affected with Alzheimer's disease. The addition of exogenous phosphatidylethanolamine resulted in no modification in the methylation rate as compared with that of endogenous PE. The addition of phosphatidyl-N-monomethylethanolamine and phosphatidyl-N,N-dimethylethanolamine resulted in significantly increased rates of methylation in brain tissues. However, the increased rate of phosphatidylethanolamine-N-methyltransferase activity stimulated by exogenous phospholipids was lower in the frontal cortex of brains with Alzheimer's disease when compared to the normals and there was no difference in the occipital cortex between Alzheimer's disease and the control. It is plausible that the decreased activity of phosphatidylethanolamine-N-methyltransferase and its low compensating ability could relate to the modification of phosphatidylcholine in brain tissues from Alzheimer's disease patients.     

Disrupted functional brain connectivity in partial epilepsy: a resting-state fMRI study

Examining the spontaneous activity to understand the neural mechanism of brain disorder is a focus in recent resting-state fMRI. In the current study, to investigate the alteration of brain functional connectivity in partial epilepsy in a systematical way, two levels of analyses (functional connectivity analysis within resting state networks (RSNs) and functional network connectivity (FNC) analysis) were carried out on resting-state fMRI data acquired from the 30 participants including 14 healthy controls(HC) and 16 partial epilepsy patients. According to the etiology, all patients are subdivided into temporal lobe epilepsy group (TLE, included 7 patients) and mixed partial epilepsy group (MPE, 9 patients). Using group independent component analysis, eight RSNs were identified, and selected to evaluate functional connectivity and FNC between groups. Compared with the controls, decreased functional connectivity within all RSNs was found in both TLE and MPE. However, dissociating patterns were observed within the 8 RSNs between two patient groups, i.e, compared with TLE, we found decreased functional connectivity in 5 RSNs increased functional connectivity in 1 RSN, and no difference in the other 2 RSNs in MPE. Furthermore, the hierarchical disconnections of FNC was found in two patient groups, in which the intra-system connections were preserved for all three subsystems while the lost connections were confined to intersystem connections in patients with partial epilepsy. These findings may suggest that decreased resting state functional connectivity and disconnection of FNC are two remarkable characteristics of partial epilepsy. The selective impairment of FNC implicated that it is unsuitable to understand the partial epilepsy only from global or local perspective. We presumed that studying epilepsy in the multi-perspective based on RSNs may be a valuable means to assess the functional changes corresponding to specific RSN and may contribute to the understanding of the neuro-pathophysiological mechanism of epilepsy.     

Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near-infrared spectroscopy analysis

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.