Resting-state Connectivity Analysis Research Articles

Gray matter deficits and resting-state abnormalities in abstinent heroin-dependent individuals

Previous neuroimaging studies have demonstrated both structural and functional damages in heroin-dependent individuals. However, few studies investigated gray matter deficits and abnormal resting-state networks together in heroin-dependent individuals. In the present study, voxel-based morphometry (VBM) was used to identify brain regions with gray matter density reduction. Resting-state fMRI connectivity analysis was employed to assess potential functional abnormalities during resting-state. All clinical significances were investigated by examining their association with duration of heroin use. Compared with healthy subjects, heroin-dependent individuals showed significant reduction in gray matter density in the right dorsolateral prefrontal cortex (DLPFC) and a decrease in resting-state functional connectivity between the right DLPFC and left inferior parietal lobe (IPL). The gray matter density of the right DLPFC and its resting-state functional connectivity with the left IPL both showed significantly negative correlation with duration of heroin use, which were likely to be related to the functional impairments in decision-making and cognitive control exhibited by heroin-dependent individuals. Our findings demonstrated that long heroin dependence impairs the right DLPFC in heroin-dependent individuals, including structural deficits and resting-state functional impairments.

Activity-based Seed Regions for Resting-state fMRI Analysis are Susceptible to Large Vessels

Introduction: Many functional magnetic resonance imaging (fMRI) analyses of the resting state rely on the identification of a seed region based on location of significant activity during a separate task; however, it is known that task-related BOLD fMRI is susceptible to the locations of large vessels [1]. Alternatively, independent component analysis (ICA) approaches identify resting-state networks without the need for seed regions [2]; however, the determination of the appropriate number of components and their final combination is not straightforward. Therefore, in investigations of restingstate connectivity within a specific network, a seed-based approach remains a method of choice. The objective of this study was to compare the location of seed regions for resting-state connectivity based on separate task-related maps to those based on anatomical landmarks. Methods: Seven healthy volunteers participated in the study after providing informed consent. Resting-state T2*-weighted images (GRE-EPI: TR/TE = 1500/30 ms, 24-cm FOV; 64x64 matrix; twenty 5-mm thick slices, 110 volumes) were collected while subjects lay still visually fixating on a small black cross at the center of a projection screen (Avotec, Inc., Stuart, FL). Additional images were collected during the performance of a bilateral finger movement task (five visually-cued epochs of 20 seconds of self-paced finger movements and 20 seconds of rest). T1-weighted anatomical images were also collected. For the fMRI data collected during finger movements, locations of significant brain activity were determined using General Linear Model (FSL: http://www.fmrib.ox.ac.uk/fsl). Two methods of seed and target selection were investigated: 1) using the resulting bilateral finger movement activity map to select the N most significant voxels within each of the left (seed) and right (target) primary sensorimotor cortices (N was decreased from 300 to 200 to 100 voxels); 2) using anatomical images to segment the hand region of each of the left (seed) and right (target) primary sensorimotor cortices based on known anatomical landmarks. The sizes of these anatomical regions were also decreased from 300 to 200 to 100 voxels by successively calculating the correlation between the time course of each voxel within the region with their average time course, and then discarding the voxel exhibiting the lowest correlation. All images and seeds were transformed to the common space of the Montreal Neurological Institute (MNI) brain, and the x, y, and z coordinates of the centre of gravity of each seed region were recorded for each subject. A repeated-measure analysis of variance (ANOVA) was then performed for each of the x, y, and z coordinates of the right and left motor cortex seeds, with method of seed selection (anatomy-based, task-based) and seed size (300, 200, 100 voxels) as within-subject factors. Results: The top figure shows the anatomical location of seeds within the motor cortex for task-based and anatomical-based identification, averaged over all subjects. The bottom figure shows the average percentage overlap between anatomicalbased and task-based seeds as a function of seed size for left (blue) and right (green) motor cortex, averaged over all subjects. Overlap is not considerable, especially for smaller seeds, suggesting that the two methods do not converge to the same area of motor cortex. As seed size decreases, taskbased seeds move to more superior locations in the brain, while anatomy-based masks moves toward inferior regions of motor cortex. In right motor cortex, the ANOVA revealed a significant effect of seed selection method on the z coordinate of the seed [F(1,6)=13.70; p<0.01]. The same trend was observed for the z coordinate in the left motor cortex [F(1,6)=5.42; p=0.059]. Conclusion Task-based seeds for resting-state connectivity analysis are located at more superior locations in proximity to large draining vessels, compared to anatomy-based seeds. Hence, seed regions based on brain activity in response to tasks may not be optimal for analysis of resting-state networks. Reference: [1] C-H Moon, et al. J Neurosci. 2007;27:6892-6902. [2] M De Luca, et al. Neuroimage. 2006;29:1359-67.

Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.

Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

OPERATIVE TREATMENT FOR HAEMORRHOIDS

Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.