Resting state functional connectivity in perfusion imaging: correlation maps with BOLD connectivity and resting state perfusion

Functional connectivity is a property of the resting state that may provide biomarkers of brain function and individual differences. Classically, connectivity is estimated as the temporal correlation of spontaneous fluctuations of BOLD signal. We investigated differences in connectivity estimated from the BOLD and CBF signal present in volumes acquired with arterial spin labeling technique in a large sample (N = 265) of healthy individuals. Positive connectivity was observable in both BOLD and CBF signal, and was present in the CBF signal also at frequencies lower than 0.009 Hz, here investigated for the first time. Negative connectivity was more variable. The validity of positive connectivity was confirmed by the existence of correlation across individuals in its intensity estimated from the BOLD and CBF signal. In contrast, there was little or no correlation across individuals between intensity of connectivity and mean perfusion levels, suggesting that these two biomarkers correspond to distinct sources of individual differences.     

Structural integrity of the uncinate fasciculus and resting state functional connectivity of the ventral prefrontal cortex in late life depression

Background

Neuroimaging studies in late life depression have reported decreased structural integrity of white matter tracts in the prefrontal cortex. Functional studies have identified changes in functional connectivity among several key areas involved in mood regulation. Few studies have combined structural and functional imaging. In this study we sought to examine the relationship between the uncinate fasciculus, a key fronto-temporal tract and resting state functional connectivity between the ventral prefrontal cortex ((PFC) and limbic and striatal areas.

Methods

The sample consisted of 24 older patients remitted from unipolar major depression. Each participant had a magnetic resonance imaging brain scan using standardized protocols to obtain both diffusion tensor imaging and resting state functional connectivity data. Our statistical approach compared structural integrity of the uncinate fasciculus and functional connectivity data.

Results

We found positive correlations between left uncinate fasciculus (UF) fractional anisotropy (FA) and resting state functional connectivity (rsFC) between the left ventrolateral PFC and left amygdala and between the left ventrolateral PFC and the left hippocampus. In addition, we found a significant negative correlation between left ventromedial PFC-caudate rsFC and left UF FA. The right UF FA did not correlate with any of the seed region based connectivity.

Conclusions

These results support the notion that resting state functional connectivity reflects structural integrity, since the ventral PFC is structurally connected to temporal regions by the UF. Future studies should include larger samples of patients and healthy comparison subjects in which both resting state and task-based functional connectivity are examined.     

Changes in resting neural connectivity during propofol sedation

Background

The default mode network consists of a set of functionally connected brain regions (posterior cingulate, medial prefrontal cortex and bilateral parietal cortex) maximally active in functional imaging studies under “no task” conditions. It has been argued that the posterior cingulate is important in consciousness/awareness, but previous investigations of resting interactions between the posterior cingulate cortex and other brain regions during sedation and anesthesia have produced inconsistent results.

Methodology/Principal Findings

We examined the connectivity of the posterior cingulate at different levels of consciousness. “No task” fMRI (BOLD) data were collected from healthy volunteers while awake and at low and moderate levels of sedation, induced by the anesthetic agent propofol. Our data show that connectivity of the posterior cingulate changes during sedation to include areas that are not traditionally considered to be part of the default mode network, such as the motor/somatosensory cortices, the anterior thalamic nuclei, and the reticular activating system.

Conclusions/Significance

This neuroanatomical signature resembles that of non-REM sleep, and may be evidence for a system that reduces its discriminable states and switches into more stereotypic patterns of firing under sedation.     

Altered cerebellar-cerebral functional connectivity in geriatric depression

Although volumetric and activation changes in the cerebellum have frequently been reported in studies on major depression, its role in the neural mechanism of depression remains unclear. To understand how the cerebellum may relate to affective and cognitive dysfunction in depression, we investigated the resting-state functional connectivity between cerebellar regions and the cerebral cortex in samples of patients with geriatric depression (n = 11) and healthy controls (n = 18). Seed-based connectivity analyses were conducted using seeds from cerebellum regions previously identified as being involved in the executive, default-mode, affective-limbic, and motor networks. The results revealed that, compared with controls, individuals with depression show reduced functional connectivity between several cerebellum seed regions, specifically those in the executive and affective-limbic networks with the ventromedial prefrontal cortex (vmPFC) and increased functional connectivity between the motor-related cerebellum seed regions with the putamen and motor cortex. We further investigated whether the altered functional connectivity in depressed patients was associated with cognitive function and severity of depression. A positive correlation was found between the Crus II–vmPFC connectivity and performance on the Hopkins Verbal Learning Test-Revised delayed memory recall. Additionally, the vermis–posterior cinglate cortex (PCC) connectivity was positively correlated with depression severity. Our results suggest that cerebellum–vmPFC coupling may be related to cognitive function whereas cerebellum–PCC coupling may be related to emotion processing in geriatric depression.     

Recursive cluster elimination based support vector machine for disease state prediction using resting state functional and effective brain connectivity

Background

Brain state classification has been accomplished using features such as voxel intensities, derived from functional magnetic resonance imaging (fMRI) data, as inputs to efficient classifiers such as support vector machines (SVM) and is based on the spatial localization model of brain function. With the advent of the connectionist model of brain function, features from brain networks may provide increased discriminatory power for brain state classification.

Methodology/Principal Findings

In this study, we introduce a novel framework where in both functional connectivity (FC) based on instantaneous temporal correlation and effective connectivity (EC) based on causal influence in brain networks are used as features in an SVM classifier. In order to derive those features, we adopt a novel approach recently introduced by us called correlation-purged Granger causality (CPGC) in order to obtain both FC and EC from fMRI data simultaneously without the instantaneous correlation contaminating Granger causality. In addition, statistical learning is accelerated and performance accuracy is enhanced by combining recursive cluster elimination (RCE) algorithm with the SVM classifier. We demonstrate the efficacy of the CPGC-based RCE-SVM approach using a specific instance of brain state classification exemplified by disease state prediction. Accordingly, we show that this approach is capable of predicting with 90.3% accuracy whether any given human subject was prenatally exposed to cocaine or not, even when no significant behavioral differences were found between exposed and healthy subjects.

Conclusions/Significance

The framework adopted in this work is quite general in nature with prenatal cocaine exposure being only an illustrative example of the power of this approach. In any brain state classification approach using neuroimaging data, including the directional connectivity information may prove to be a performance enhancer. When brain state classification is used for disease state prediction, our approach may aid the clinicians in performing more accurate diagnosis of diseases in situations where in non-neuroimaging biomarkers may be unable to perform differential diagnosis with certainty.     

Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study

Aberrant topological properties of small-world human brain networks in patients with schizophrenia (SZ) have been documented in previous neuroimaging studies. Aberrant functional network connectivity (FNC, temporal relationships among independent component time courses) has also been found in SZ by a previous resting state functional magnetic resonance imaging (fMRI) study. However, no study has yet determined if topological properties of FNC are also altered in SZ. In this study, small-world network metrics of FNC during the resting state were examined in both healthy controls (HCs) and SZ subjects. FMRI data were obtained from 19 HCs and 19 SZ. Brain images were decomposed into independent components (ICs) by group independent component analysis (ICA). FNC maps were constructed via a partial correlation analysis of ICA time courses. A set of undirected graphs were built by thresholding the FNC maps and the small-world network metrics of these maps were evaluated. Our results demonstrated significantly altered topological properties of FNC in SZ relative to controls. In addition, topological measures of many ICs involving frontal, parietal, occipital and cerebellar areas were altered in SZ relative to controls. Specifically, topological measures of whole network and specific components in SZ were correlated with scores on the negative symptom scale of the Positive and Negative Symptom Scale (PANSS). These findings suggest that aberrant architecture of small-world brain topology in SZ consists of ICA temporally coherent brain networks.     

Resting state functional connectivity in patients with chronic hallucinations

Auditory verbal hallucinations (AVH) are not only among the most common but also one of the most distressing symptoms of schizophrenia. Despite elaborate research, the underlying brain mechanisms are as yet elusive. Functional MRI studies have associated the experience of AVH with activation of bilateral language-related areas, in particular the right inferior frontal gyrus (rIFG) and the left superior temporal gyrus (lSTG). While these findings helped to understand the neural underpinnings of hearing voices, they provide little information about possible brain mechanisms that predispose a person to experience AVH, i.e. the traits to hallucinate. In this study, we compared resting state connectivity between 49 psychotic patients with chronic AVH and 49 matched controls using the rIFG and the lSTG as seed regions, to identify functional brain systems underlying the predisposition to hallucinate. The right parahippocampal gyrus showed increased connectivity with the rIFG in patients as compared to controls. Reduced connectivity with the rIFG in patients was found for the right dorsolateral prefrontal cortex. Reduced connectivity with the lSTG in patients was identified in the left frontal operculum as well as the parietal opercular area. Connectivity between the lSTG and the left hippocampus was also reduced in patients and showed a negative correlation with the severity of hallucinations. Concluding, we found aberrant connectivity between the seed regions and medial temporal lobe structures which have a prominent role in memory retrieval. Moreover, we found decreased connectivity between language-related areas, indicating aberrant integration in this system potentially including corollary discharge mechanisms.     

Evaluating the effective connectivity of resting state networks using conditional Granger causality

The human brain has been documented to be spatially organized in a finite set of specific coherent patterns, namely resting state networks (RSNs). The interactions among RSNs, being potentially dynamic and directional, may not be adequately captured by simple correlation or anticorrelation. In order to evaluate the possible effective connectivity within those RSNs, we applied a conditional Granger causality analysis (CGCA) to the RSNs retrieved by independent component analysis (ICA) from resting state functional magnetic resonance imaging (fMRI) data. Our analysis provided evidence for specific causal influences among the detected RSNs: default-mode, dorsal attention, core, central-executive, self-referential, somatosensory, visual, and auditory networks. In particular, we identified that self-referential and default-mode networks (DMNs) play distinct and crucial roles in the human brain functional architecture. Specifically, the former RSN exerted the strongest causal influence over the other RSNs, revealing a top-down modulation of self-referential mental activity (SRN) over sensory and cognitive processing. In quite contrast, the latter RSN was profoundly affected by the other RSNs, which may underlie an integration of information from primary function and higher level cognition networks, consistent with previous task-related studies. Overall, our results revealed the causal influences among these RSNs at different processing levels, and supplied information for a deeper understanding of the brain network dynamics.     

Structural and resting state functional connectivity of the subthalamic nucleus: identification of motor STN parts and the hyperdirect pathway

Deep brain stimulation (DBS) for Parkinson's disease often alleviates the motor symptoms, but causes cognitive and emotional side effects in a substantial number of cases. Identification of the motor part of the subthalamic nucleus (STN) as part of the presurgical workup could minimize these adverse effects. In this study, we assessed the STN's connectivity to motor, associative, and limbic brain areas, based on structural and functional connectivity analysis of volunteer data. For the structural connectivity, we used streamline counts derived from HARDI fiber tracking. The resulting tracks supported the existence of the so-called "hyperdirect" pathway in humans. Furthermore, we determined the connectivity of each STN voxel with the motor cortical areas. Functional connectivity was calculated based on functional MRI, as the correlation of the signal within a given brain voxel with the signal in the STN. Also, the signal per STN voxel was explained in terms of the correlation with motor or limbic brain seed ROI areas. Both right and left STN ROIs appeared to be structurally and functionally connected to brain areas that are part of the motor, associative, and limbic circuit. Furthermore, this study enabled us to assess the level of segregation of the STN motor part, which is relevant for the planning of STN DBS procedures.     

Cortical network dynamics with time delays reveals functional connectivity in the resting brain

In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present paper we test the hypothesis that time delays in the network dynamics play a crucial role in the generation of these fluctuations. By tuning the propagation velocity in a network based on primate connectivity, we scale the time delays and demonstrate the emergence of the resting state networks for biophysically realistic parameters.     

Energy-based stochastic control of neural mass models suggests time-varying effective connectivity in the resting state

Several studies posit energy as a constraint on the coding and processing of information in the brain due to the high cost of resting and evoked cortical activity. This suggestion has been addressed theoretically with models of a single neuron and two coupled neurons. Neural mass models (NMMs) address mean-field based modeling of the activity and interactions between populations of neurons rather than a few neurons. NMMs have been widely employed for studying the generation of EEG rhythms, and more recently as frameworks for integrated models of neurophysiology and functional MRI (fMRI) responses. To date, the consequences of energy constraints on the activity and interactions of ensembles of neurons have not been addressed. Here we aim to study the impact of constraining energy consumption during the resting-state on NMM parameters. To this end, we first linearized the model, then used stochastic control theory by introducing a quadratic cost function, which transforms the NMM into a stochastic linear quadratic regulator (LQR). Solving the LQR problem introduces a regime in which the NMM parameters, specifically the effective connectivities between neuronal populations, must vary with time. This is in contrast to current NMMs, which assume a constant parameter set for a given condition or task. We further simulated energy-constrained stochastic control of a specific NMM, the Wilson and Cowan model of two coupled neuronal populations, one of which is excitatory and the other inhibitory. These simulations demonstrate that with varying weights of the energy-cost function, the NMM parameters show different time-varying behavior. We conclude that constraining NMMs according to energy consumption may create more realistic models. We further propose to employ linear NMMs with time-varying parameters as an alternative to traditional nonlinear NMMs with constant parameters.     

Changes in the structure and the functional state of thylakoids under the conditions of osmotic shock

The effect of osmotic shock on the ultrastructure and functions of C-class pea chloroplasts has been examined. When incubated in a non-sucrose medium for 30 s or more, thylakoids were found to pass to a stable deformed state. This state was characterized by an altered orientation of thylakoids to each other with the lumen thickness remaining the same as in the normal state. Experiments with shorter incubation periods (10–20 s) revealed a swelling of thylakoids, which probably represented an intermediate stage. The deformation of the thylakoid system was accompanied by a decrease in the non-cyclic ATP synthesis but by an increase in the rate of cyclic photophosphorylation. Besides, the deformed thylakoids demonstrated an acceleration of the basal electron transport, as well a rise in the light-induced H⁺ and imidazol uptake. The data obtained are discussed in the light of membrane interactions fixing the configuration of a thylakoid.     

Resting‐state functional connectivity measured by diffuse correlation spectroscopy

Near‐infrared diffuse correlation spectroscopy (DCS) is used to record spontaneous cerebral blood flow fluctuations in the frontal cortex. Nine adult subjects participated in the experiments, in which 8‐minute spontaneous fluctuations were simultaneously recorded from the left and right dorsolateral and inferior frontal regions. Resting‐state functional connectivity (RSFC) was measured by the temporal correlation of the low frequency fluctuations. Our data shows the RSFC within the dorsolateral region is significantly stronger than that between the inferior and dorsolateral regions, in line with previous observations with functional near‐infrared spectroscopy. This indicates that DCS is capable of investigating brain functional connectivity in terms of cerebral blood flow.      

Climate extremes drive changes in functional community structure

The response of communities to climate extremes can be quite variable. Much of this variation has been attributed to differences in community‐specific functional trait diversity, as well as community composition. Yet, few if any studies have explicitly tested the response of the functional trait structure of communities following climate extremes (CEs). Recently in South Florida, two independent, but sequential potential CEs took place, a 2010 cold front, followed by a 2011 drought, both of which had profound impacts on a subtropical estuarine fish community. These CEs provided an opportunity to test whether the structure of South Florida fish communities following each extreme was a result of species‐specific differences in functional traits. From historical temperature (1927–2012) and freshwater inflows records into the estuary (1955–2012), we determined that the cold front was a statistically extreme disturbance, while the drought was not, but rather a decadal rare disturbance. The two disturbances predictably affected different parts of functional community structure and thus different component species. The cold front virtually eliminated tropical species, including large‐bodied snook, mojarra species, nonnative cichlids, and striped mullet, while having little affect on temperate fishes. Likewise, the drought severely impacted freshwater fishes including Florida gar, bowfin, and two centrarchids, with little effect on euryhaline species. Our findings illustrate the ability of this approach to predict and detect both the filtering effects of different types of disturbances and the implications of the resulting changes in community structure. Further, we highlight the value of this approach to developing predictive frameworks for better understanding community responses to global change.     

水文连通性对西洞庭湖大型底栖动物群落结构的影响

人为活动干扰纵向与横向水文连通性,对全球景观的生态完整性造成威胁。全球大型河流的洪泛区,包括中国最大的河流长江,都面临着水文连通性下降而造成的生物多样性灾难性损失。了解水文连通性如何影响生物在生境之间的扩散潜力,对于恢复和维持区域和局地生物多样性至关重要。评估了水文连通度对西洞庭湖大型底栖动物群落的影响。沿连通性梯度对54个地点进行了采样,包括河流27个样点,连通湖泊9个样点,阻隔湖泊9个样点和改造洲滩9个样点。结果表明,丰富度,Shannon-wiener指数和Margalef指数等α多样性指数之间存在显着差异(P<0.05),其中连通湖泊与阻隔湖泊的丰富度与丰度较高。连通性最低的改造洲滩α与β多样性最小。更重要的是,丰富度替换是造成河流、连通湖泊和阻隔湖泊中群落组成变化的主要因素,这表明在扩散限制下的中性动态可能是塑造这些生境中底栖动物群落的主要过程。但是,在改造洲滩中,嵌套的重要性很高,可以与丰富度替换相提并论,这反映了在这种不利的环境中,由于生态位分化而导致物种消失的非随机过程。广义线性模型证实,大型底栖动物群落组成空间转换的关键驱动力是水文连通性。研究表明,维持水文连通...     

Changes in macroinvertebrate community structure and function along a

Aquatic macroinvertebrate communities were sampled between 1994 and 1996 at 13 sites downstream of phosphorus (P)-enriched canal inflows in a northern Everglades marsh to determine the effects of nutrient enrichment on community structure and function. Sampling was performed using sweep nets and Hester–Dendy (HD) samplers. Data were analyzed to assess changes in taxa richness and diversity, species composition, and functional group composition along the gradient. Environmental conditions at each site were characterized to interpret spatial changes in these metrics. Mean water-column total P (TP) increased from 10g l^minus;1at sites in the marsh interior to as high as 160g l^minus;1at sites closest to the canal. Vegetation and habitat composition changed dramatically along the gradient, with sawgrass and slough-wet prairie habitats accounting for most vegetative cover in the interior and cattail accounting for nearly 100%of the cover near the canal. These differences in TP concentrations and vegetation were used to classify sites as reference, enriched, and highly enriched. Daytime dissolved oxygen (DO) concentrations averaged 3mg l^minus;1at reference sites as compared with concentrations 2mg l^minus;1at enriched and highly enriched sites. Total macroinvertebrate densities were significantly higher in sweep samples and significantly lower in HD samples from highly enriched sites as compared with the reference condition. Taxa richness and diversity in sweep samples did not change significantly along the gradient, but declined with enrichment on the HD samplers. Insects were the dominant organisms at all sites, but declined in percent abundance with enrichment in sweep samples due to decreases in dipterans, trichopterans and odonates and an increase in oligochaetes. Changes in major invertebrate classes were less pronounced on HD samplers, although amphipods showed significant declines with enrichment. Principal components analysis revealed a clear distinction in taxonomic composition between reference sites and both enriched and highly enriched sites for sweep samples as common chironomid taxa at reference sites declined with enrichment while pollution-tolerant chironomid and oligochaete taxa increased. A similar, but less dramatic trend was found for HD samples, with selected amphipod, chironomid, and gastropod taxa declining with enrichment and pollution-tolerant taxa reaching peak abundance at enriched sites. The functional composition in sweep samples showed modest changes with enrichment, including a shift in dominance from epibenthic collector–gatherers/deposit feeders, which were predominantly chironomids, to subsurface taxa, which were predominantly oligochaetes. Shifts in invertebrate functional composition on HD samplers with enrichment were attributable to declines in the dominance of shredders and collector-filterer/suspension-feeders. Portions of the Everglades exposed to P-enriched runoff are showing characteristic shifts in macroinvertebrate taxonomic composition related to eutrophication. This shift has occurred without a change in species diversity and with an increase in total invertebrate abundance indicative of an overall increase in marsh productivity. The transition from an oligotrophic to eutrophic community signals a decline in the biological integrity of the Everglades ecosystem in response to P enrichment.     

Clustering of resting state networks

Background

The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm.

Methodology/Principal Findings

The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization.

Conclusions/Significance

The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.