Voxel Pairs Research Articles

High-dimensional tests for functional networks of brain anatomic regions.

Exploring resting-state brain functional connectivity of autism spectrum disorders (ASD) using functional magnetic resonance imaging (fMRI) data has become a popular topic over the past few years. The data in a standard brain template consist of over 170,000 voxel specific points in time for each human subject. Such an ultra-high dimensionality makes the voxel-level functional connectivity analysis (involving four billion voxel pairs) both statistically and computationally inefficient. In this work, we introduce a new framework to identify the functional brain network at the anatomical region level for each individual. We propose two pairwise tests to detect region dependence, and one multiple testing procedure to identify global structures of the network. The limiting null distribution of each test statistic is derived. It is also shown that the tests are rate optimal when the alternative block networks are sparse. The numerical studies show that the proposed tests are valid and powerful. We apply our method to a resting-state fMRI study on autism and identify patient-unique and control-unique hub regions. These findings are biologically meaningful and consistent with the existing literature.

The Voxel-Wise Functional Connectome Can Be Efficiently Derived from Co-activations in a Sparse Spatio-Temporal Point-Process

Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this “functional connectome” based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series consist of all-or-none temporally localized events, analogous to the avalanches of neural activity observed in recordings of local field potentials (LFP), an observation of potentially high neurobiological relevance.

A Bayesian hierarchical framework for modeling brain connectivity for neuroimaging data.

We propose a novel Bayesian hierarchical model for brain imaging data that unifies voxel-level (the most localized unit of measure) and region-level brain connectivity analyses, and yields population-level inferences. Functional connectivity generally refers to associations in brain activity between distinct locations. The first level of our model summarizes brain connectivity for cross-region voxel pairs using a two-component mixture model consisting of connected and nonconnected voxels. We use the proportion of connected voxel pairs to define a new measure of connectivity strength, which reflects the breadth of between-region connectivity. Furthermore, we evaluate the impact of clinical covariates on connectivity between region-pairs at a population level. We perform parameter estimation using Markov chain Monte Carlo (MCMC) techniques, which can be executed quickly relative to the number of model parameters. We apply our method to resting-state functional magnetic resonance imaging (fMRI) data from 32 subjects with major depression and simulated data to demonstrate the properties of our method.

Resting-state functional connectivity abnormalities correlate with psychometric hepatic encephalopathy score in cirrhosis

Background & aimsNeurocognitive impairment is a common complication of cirrhosis and regarded as the important characteristic for early stage of hepatic encephalopathy (HE). This study aimed to investigate the changes in brain network centrality of functional connectivity among cirrhotic patients and uncover the mechanisms about early HE. MethodsTwenty-four cirrhotic patients without overt HE and 21 healthy controls were enrolled and underwent resting-state fMRI and Psychometric Hepatic Encephalopathy Score (PHES) tests. Whole-brain functional network was constructed by measuring the temporal correlations of every pairs of brain gray matter voxels; and then voxel-wise degree centrality (DC), an index reflecting importance of a node in functional integration, was calculated and compared between two groups. A seed-based resting-state functional connectivity (RSFC) analysis was further performed to investigate abnormal functional connectivity pattern of those regions with changed DC. ResultsCompared with controls, the cirrhotic patients had worse performances in all neurocognitive tests and lower PHES score. Meanwhile, patients showed decreased DC in bilateral medial prefrontal gyrus and anterior cingulate cortex, left middle frontal gyrus, and bilateral thalamus; while increased DC in right middle occipital gyrus and parahippocampal gyrus/inferior temporal gyrus. The seed-based RSFC analyses revealed that the relevant functional networks, such as default-mode and attention networks, visual network, and thalamo-cortical circuits, were disturbed in cirrhotic patients. The DC changes were correlated with PHES score in patient group. ConclusionsOur findings further confirm brain network disorganization in cirrhotic patients with neurocognitive impairments and may provide a new perspective for understanding HE-related mechanisms.

Altered cortical hubs in functional brain networks in amyotrophic lateral sclerosis.

Cortical hubs are highly connected nodes in functional brain networks that play vital roles in the efficient transfer of information across brain regions. Although altered functional connectivity has been found in amyotrophic lateral sclerosis (ALS), the changing pattern in functional network hubs in ALS remains unknown. In this study, we applied a voxel-wise method to investigate the changing pattern of cortical hubs in ALS. Through resting-state fMRI, we constructed whole-brain voxel-wise functional networks by measuring the temporal correlations of each pair of brain voxels and identified hubs using the graph theory method. Specifically, a functional connectivity strength (FCS) map was derived from the data on 20 patients with ALS and 20 healthy controls. The brain regions with high FCS values were regarded as functional network hubs. Functional hubs were found mainly in the bilateral precuneus, parietal cortex, medial prefrontal cortex, and in several visual regions and temporal areas in both groups. Within the hub regions, the ALS patients exhibited higher FCS in the prefrontal cortex compared with the healthy controls. The FCS value in the significantly abnormal hub regions was correlated with clinical variables. Results indicated the presence of altered cortical hubs in the ALS patients and could therefore shed light on the pathophysiology mechanisms underlying ALS.

Efficient Estimation of Three-Dimensional Covariance and its Application in the Analysis of Heterogeneous Samples in Cryo-Electron Microscopy

Single-particle cryogenic electron microscopy (cryo-EM) is a powerful tool for the study of macromolecular structures at high resolution. Classification allows multiple structural states to be extracted and reconstructed from the same sample. One classification approach is via the covariance matrix, which captures the correlation between every pair of voxels. Earlier approaches employ computing-intensive resampling and estimate only the eigenvectors of the matrix, which are then used in a separate fast classification step. We propose an iterative scheme to explicitly estimate the covariance matrix in its entirety. In our approach, the flexibility in choosing the solution domain allows us to examine a part of the molecule in greater detail. Three-dimensional covariance maps obtained in this way from experimental data (cryo-EM images of the eukaryotic pre-initiation complex) prove to be in excellent agreement with conclusions derived by using traditional approaches, revealing in addition the interdependencies of ligand bindings and structural changes.

Synchronization, non-linear dynamics and low-frequency fluctuations: analogy between spontaneous brain activity and networked single-transistor chaotic oscillators.

In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D2), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.

Disrupted cortical hubs in functional brain networks in social anxiety disorder

ObjectiveThe network hubs, characterized by the large number of connections to other regions, play important roles in the proper and effective transfer of information. Previous functional neuroimaging studies have demonstrated that patients with social anxiety disorder (SAD) have aberrant functional connectivity. The changing pattern in functional network hubs in SAD, however, remains incompletely understood. MethodsTwenty SAD patients and 20 matched healthy controls were recruited. Resting-state fMRI data were obtained using a gradient-recalled echo-planar imaging sequence. Whole-brain voxel-wise functional networks were constructed by measuring the temporal correlations of each pair of brain voxels and then hubs were identified by using the graph theory method. Specifically, a functional connectivity strength (FCS) map was computed in each subject and the regions with higher FCS value were considered as functional network hubs. ResultsCompared with healthy controls, SAD patients showed significantly decreased FCS in the bilateral precuneus and significantly increased FCS in the right fusiform gyrus. Furthermore, a significantly negative correlation was observed between the FCS value in the precuneus and the illness duration. ConclusionThe present study demonstrated for the first time that disrupted cortical hubs existed in patients with SAD during resting state. SignificanceThese findings may provide novel insight into understanding of pathophysiological mechanisms underlying SAD.

Connexel visualization: a software implementation of glyphs and edge-bundling for dense connectivity data using brainGL

The visualization of brain connectivity becomes progressively more challenging as analytic and computational advances begin to facilitate connexel-wise analyses, which include all connections between pairs of voxels. Drawing full connectivity graphs can result in depictions that, rather than illustrating connectivity patterns in more detail, obfuscate patterns owing to the data density. In an effort to expand the possibilities for visualization, we describe two approaches for presenting connexels: edge-bundling, which clarifies structure by grouping geometrically similar connections; and, connectivity glyphs, which depict a condensed connectivity map at each point on the cortical surface. These approaches can be applied in the native brain space, facilitating interpretation of the relation of connexels to brain anatomy. The tools have been implemented as part of brainGL, an extensive open-source software for the interactive exploration of structural and functional brain data.

Automatic Segmentation of Pulmonary Fissures in Computed Tomography Images Using 3D Surface Features

Pulmonary interlobar fissures are important anatomic structures in human lungs and are useful in locating and classifying lung abnormalities. Automatic segmentation of fissures is a difficult task because of their low contrast and large variability. We developed a fully automatic training-free approach for fissure segmentation based on the local bending degree (LBD) and the maximum bending index (MBI). The LBD is determined by the angle between the eigenvectors of two Hessian matrices for a pair of adjacent voxels. It is used to construct a constraint to extract the candidate surfaces in three-dimensional (3D) space. The MBI is a measure to discriminate cylindrical surfaces from planar surfaces in 3D space. Our approach for segmenting fissures consists of five steps, including lung segmentation, plane-like structure enhancement, surface extraction with LBD, initial fissure identification with MBI, and fissure extension based on local plane fitting. When applying our approach to 15 chest computed tomography (CT) scans, the mean values of the positive predictive value, the sensitivity, the root-mean square (RMS) distance, and the maximal RMS are 91%, 88%, 1.01 ± 0.99mm, and 11.56mm, respectively, which suggests that our algorithm can efficiently segment fissures in chest CT scans.

Arterial Input Function Derived from Pairwise Correlations Between PET-image Voxels

A metabolite corrected arterial input function is a prerequisite for quantification of positron emission tomography (PET) data by compartmental analysis. This quantitative approach is also necessary for radioligands without suitable reference regions in brain. The measurement is laborious and requires cannulation of a peripheral artery, a procedure that can be associated with patient discomfort and potential adverse events. A non invasive procedure for obtaining the arterial input function is thus preferable. In this study, we present a novel method to obtain image-derived input functions (IDIFs). The method is based on calculation of the Pearson correlation coefficient between the time-activity curves of voxel pairs in the PET image to localize voxels displaying blood-like behavior. The method was evaluated using data obtained in human studies with the radioligands [(11)C]flumazenil and [(11)C]AZ10419369, and its performance was compared with three previously published methods. The distribution volumes (VT) obtained using IDIFs were compared with those obtained using traditional arterial measurements. Overall, the agreement in VT was good (∼3% difference) for input functions obtained using the pairwise correlation approach. This approach performed similarly or even better than the other methods, and could be considered in applied clinical studies. Applications to other radioligands are needed for further verification.

Overlapping and segregated resting‐state functional connectivity in patients with major depressive disorder with and without childhood neglect

Many studies have suggested that childhood maltreatment increase risk of adulthood major depressive disorder (MDD) and predict its unfavorable treatment outcome, yet the neural underpinnings associated with childhood maltreatment in MDD remain poorly understood. Here, we seek to investigate the whole-brain functional connectivity patterns in MDD patients with childhood maltreatment. Resting-state functional magnetic resonance imaging was used to explore intrinsic or spontaneous functional connectivity networks of 18 MDD patients with childhood neglect, 20 MDD patients without childhood neglect, and 20 healthy controls. Whole-brain functional networks were constructed by measuring the temporal correlations of every pairs of brain voxels and were further analyzed by using graph-theory approaches. Relative to the healthy control group, the two MDD patient groups showed overlapping reduced functional connectivity strength in bilateral ventral medial prefrontal cortex/ventral anterior cingulate cortex. However, compared with MDD patients without a history of childhood maltreatment, those patients with such a history displayed widespread reduction of functional connectivity strength primarily in brain regions within the prefrontal-limbic-thalamic-cerebellar circuitry, and these reductions significantly correlated with measures of childhood neglect. Together, we showed that the MDD groups with and without childhood neglect exhibited overlapping and segregated functional connectivity patterns in the whole-brain networks, providing empirical evidence for the contribution of early life stress to the pathophysiology of MDD.

How many clusters in the insular cortex?

How many clusters in the insular cortex?

Cognition and cannabis: from anecdote to advanced technology

Whether the use of cannabis causes lasting cognitive or behavioural alteration has been controversial for decades. Studies addressing the problem have often been confounded by failure to account for residual acute effects or withdrawal and by failure to measure cognitive function before drug use. Investigative strategies have included neuropsychological testing, brain activation during cognitive tasks, epidemiology and identification of morphological alteration in the brains of cannabis users. In this issue of Brain , Zalesky et al. (2012) apply a novel strategy—diffusion-weighted MRI and connectivity mapping—to demonstrate microstructural alterations affecting brain axonal pathways in long-term cannabis users. Without predefined regions of interest, thousands of voxel pairs were compared, and impaired axonal connectivity was identified in the fimbria of the hippocampus and the splenium of the corpus callosum. These abnormalities were greatest in subjects who began regular marijuana use during early adolescence and were present in white matter structures in which cannabinoid (CB)1 receptor density is maximal in utero and during childhood. The findings are consistent with the endocannabinoid system playing a key role in brain development, and …

Effects of Age on the Structure of Functional Connectivity Networks During Episodic and Working Memory Demand

The aim of the study was to investigate age-related differences in large-scale functional connectivity networks during episodic and working memory challenge. A graph theoretical approach was used providing an exhaustive set of topological measures to quantify age-related differences in the network structure on various scales. In a single session, 10 young (22-30 years) and 10 senior (62-77 years) subjects performed an episodic and a working memory task during functional magnetic resonance imaging. Networks of functional connectivity were constructed by correlating the blood oxygenation level-dependent (BOLD) signal for every pair of voxels. Statistical network parameters yield a global characterization of the network topology, the quantification of the importance of specific regions, and shifts in local connectivity. An age-related increase in the density and size of the networks and loss of small-worldness was observed, related to an expanded distribution of brain activity during both memory demands in seniors, and a more specific and localized activity in young subjects. In addition, we found highly symmetrical neural networks in young subjects accompanied by a strong coupling between parietal and occipital regions. In contrast, seniors showed pronounced left-hemispheric asymmetry with decreased connectivity within occipital areas, but increased connectivity within parietal areas. Moreover, seniors engaged an additional frontal network strongly connected to parietal areas. In contrast to young subjects, seniors showed an almost identical structure of network connectivity during both memory tasks. The chosen network approach is explorative and hypothesis-free. Our results extend seed-based and BOLD-signal intensity focused studies, and support present hypotheses like compensation and dedifferentiation.

Cortical and thalamic resting-state functional connectivity is altered in childhood absence epilepsy

Functional imaging studies have identified a common network of brain regions that activate and deactivate during the generalised spike wave (GSW) discharges of childhood absence epilepsy (CAE). Functional connectivity within this network is also altered during the resting state. In this study our aim was to assess functional connectivity throughout the whole brain of patients with CAE. We studied a group of eleven patients with untreated CAE and eleven matched controls using resting-state fMRI. We measured functional connectivity between every pair of voxels and generated images of "whole-brain" functional connectivity by counting the number of functional connections of each voxel. There were marked differences between CAE patients and controls in whole brain functional connectivity. The patients had decreased connectivity in the thalamus and basal ganglia and increased connectivity in the medial occipital cortex. These findings suggest enduring changes in function of the thalamus and the cortex in CAE patients even when there is no GSW activity. These human functional connectivity data support the findings in animal models of involvement of cortex as well as thalamus in absence epilepsy.

The relationship between regional and inter‐regional functional connectivity deficits in schizophrenia

While schizophrenia is frequently characterized as a disorder of disturbed functional connectivity, the causes and pathophysiological origins of such disturbances remain unclear. The aim of this study was to better elucidate the mechanistic causes of abnormal functional connectivity in schizophrenia, measured as the extent of temporal correlation between endogenous fluctuations recorded at anatomically discrete brain regions during resting-state functional MRI. An approach was developed to perform whole-brain connectivity mapping at the resolution of individual pairs of voxels, without the need for arbitrary parcellation of the cerebrum. Between-group connectivity reductions in 12 people diagnosed with schizophrenia and 15 age-, IQ-, and gender-matched healthy volunteers were localized to a distributed network including frontoparietal and occipitoparietal connections. The gray-matter regions comprising this disturbed network showed evidence of local reductions in both intra-regional homogeneity (29%-33% reduction) and signal power (40%-60% reduction). The extent to which inter-regional correlation was reduced between a pair of gray matter regions was found to be strongly correlated with the extent of local decoherence evident within the gray matter regions per se. This suggests measurement of aberrant functional connectivity in schizophrenia is both a measurement of altered coupling between regions as well as a measurement of local decoherence within regions.

Structural analysis of functional connectivity related to memory reveals network changes in memory function over the life span

We address the question how the function of memory changes over the life span. For this we analyse fMRI data collected for healthy individuals ageing 20 up to over 80 years during two different memory tasks. Working memory is assessed through a 2-back task, episodic memory through active remembering of autobiographic events. From the fMRI data we construct networks of functional connectivity by correlating the fMRI signal of every pair of voxels. For the resulting networks we compute structural parameters and identify important anatomic structures. The analysis reveals that the density and size of the networks increases with age, related to a stronger and wider distributed brain activity during memory tasks in elderly subjects, and a more specific and localized activity in younger individuals. We also observe a shift in the localization of regions that are involved in memory processing, and also in the connectivity patterns. While all individuals exhibit a strong connection between the frontal and the occipital lobes, elderly subjects are additionally characterized by a strong connection to parietal areas. Our analysis shows that additional brain areas become involved in memory function as age increases, and that also connectivity patterns change, hinting at either compensatory mechanisms or in artifacts related to erroneous co-activation.

The Functional Architecture of Noise Correlation in fMRI Responses from Human Visual Cortex

When an identical stimulus is presented repeatedly, the activity of sensory cortical neurons varies from trial to trial, dubbed ‘neuronal noise’. Recent electrophysiological and imaging studies reported that the ‘noise’ is not just a random and independent deviation from signal and reflects correlated activity among local cortical sites. Here we investigated the structure of correlated ‘noises’ in early human visual areas by monitoring moment-to-moment fluctuations in fMRI responses to visual stimuli. By defining receptive fields and stimulus preferences of individual voxels, we could reveal a reliable functional architecture of noise correlation: noise correlation was high in pairs of voxels whose stimulus preferences are similar and whose receptive fields are close to each other. The analysis of residual correlation confirmed that this functionally defined structure of noise correlation could not be explained by trivial correlations due to anatomical proximity. The spectral analysis of time series revealed that the stimulus-preference-dependent correlation was maximal at a low (<0.035Hz) band of temporal frequency whereas the receptive field-dependent correlation was maximal at a medium (0.035∼0.082Hz) band. Furthermore, the functional structure of noise correlation was held true for voxel pairs within and between different visual areas, regardless of the presence or types of visual stimulation.

Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer

(18)F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer. Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body (18)F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different image-derived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal-Wallis test (P < 0.05). Specificity and sensitivity (including 95% confidence intervals) for each of the studied parameters were derived using receiver-operating-characteristic curves. Relationships between pairs of voxels, characterizing local tumor metabolic nonuniformities, were able to significantly differentiate all 3 patient groups (P < 0.0006). Regional measures of tumor characteristics, such as size of nonuniform metabolic regions and corresponding intensity nonuniformities within these regions, were also significant factors for prediction of response to therapy (P = 0.0002). Receiver-operating-characteristic curve analysis showed that tumor textural analysis can provide nonresponder, partial-responder, and complete-responder patient identification with higher sensitivity (76%-92%) than any SUV measurement. Textural features of tumor metabolic distribution extracted from baseline (18)F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.