Relevant Resting-state Networks Research Articles

Ventral striatal resting-state functional connectivity in adolescents is associated with earlier onset of binge drinking

BackgroundEarlier engagement in heavy drinking during adolescence is a risk factor for the development of alcohol use disorders later in life. Longitudinal studies in adolescents have linked brain structure and task-evoked function to future alcohol use; however, less is known about how intrinsic network-level interactions relate to future substance use during this developmental period. MethodsIn this prospective longitudinal study, resting-state functional connectivity of the ventral striatum, risky decision making, and sensation seeking were measured in 73 adolescents at baseline. Participants were between the ages of 14 and 15 and had no substantial history of substance use upon study entry. Follow-up interviews were conducted approximately every 3 months to assess the initiation of binge drinking (≥ 5 or ≥ 4 drinks per occasion for males or females, respectively). ResultsAdolescents who began binge drinking sooner exhibited greater connectivity of the ventral striatum to the left precuneus, left angular gyrus, and the left superior frontal gyrus. Greater connectivity of the ventral striatum to the right insula/putamen was associated with longer duration to the onset of binge drinking. Resting-state functional connectivity in these regions was not associated with baseline assessments of risky decision making or sensation seeking. ConclusionsFindings provide novel information about potential risk factors for early initiation of heavy alcohol use. Interventions that target relevant resting-state networks may enhance prevention efforts to decrease adolescent substance use by prolonging onset to heavier levels of alcohol consumption.

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome.

Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.

Specific occupational profiles as proxies of cognitive reserve induce neuroprotection in dementia with Lewy bodies.

Cognitive reserve (CR) delays cognitive decline due to neurodegeneration. Heterogeneous evidence suggests that education may act as CR in Dementia with Lewy Bodies (DLB). No data, however, are currently available on the role of occupation as proxy of CR in this neuropathology. Thirty-three patients with probable DLB were retrospectively included. We performed regression analyses models (TFCE p< 0.05) and seed-based interregional correlation analyses (p= 0.001, FWE-corrected at cluster-level) with brain metabolism. We aimed at exploring the relationship between brain metabolic connectivity, as assessed by FDG-PET, in the relevant resting-state networks and CR proxies (education, 6-levels occupation, and the specific O*Net occupational profiles). Education modulates executive (ECN), attentive (ATTN) and posterior default mode (PDMN) networks in the highly educated DLB subjects, as shown by an increased metabolic connectivity, acting as a compensatory mechanism. High scores of the 6-levels occupation scale were associated with a decreased connectivity in the anterior default mode (ADMN) and high visual network (HVN), suggesting brain reserve mechanisms. As for the specific O*Net occupational profiles, these modulated ADMN, PDMN, ATTN, ECN, HVN and primary visual network (PVN) connectivity according to different neuroprotection mechanisms, namely neural reserve and compensation against neurodegeneration. This study highlights the relevance of life-long occupational activities at individual level in the neural expression of compensatory and neuroprotective mechanisms in DLB.

Within-network brain connectivity in Crohn's disease patients with gadolinium deposition in the cerebellum.

Patients with Crohn's disease (CD) undergo multiple gadolinium-based contrast agent injections across their lifespan to enhance signal intensity of the intestinal wall and differentiate active from quiescent inflammatory disease. Thus, CD patients are prone to gadolinium accumulation in the brain and represent a non-neurological population to explore gadolinium-related brain toxicity. Possible effects are expected to be greater on the cerebellar network due to the high propensity of the dentate nucleus to accumulate gadolinium. Herein, we provide a whole-brain network analysis of resting-state fMRI dynamics in long-term quiescent CD patients with normal renal function and MRI evidence of gadolinium deposition in the brain. Fifteen patients with CD and 16 healthy age- and gender-matched controls were enrolled in this study. Relevant resting-state networks (RSNs) were identified using independent component analysis (ICA) from functional magnetic resonance imaging data. An unpaired two-sample t test (with age and sex as nuisance variables) was used to investigate between different RSNs. Clusters were determined by using threshold-free cluster enhancement and a family-wise error corrected cluster significance threshold of p < 0.05. Patients showed significantly decreased resting-state functional connectivity (p < 0.05, FWE corrected) of several regions of the right frontoparietal (FPR) and the dorsal attention (DAN) RSNs. No differences between the two groups were found in the functional connectivity maps of all the other RSNs, including the cerebellar network. Our findings suggest a non-significant impact of gadolinium deposition on within-network cerebellar functional connectivity of long-term quiescent CD patients.

255. Automated classification of Resting State fMRI networks using Machine Learning algorithms

Purpose The need of an automatic reorganization of the massive Resting State (RS) output is impelling due to the spreading of the RS fMRI [1] , as a huge number of RS network is carried out for each subject and only a few of them are interesting. The aim of the study is to use a Machine Learning algorithm to identify four relevant networks (Default Mode, Auditory, Visual, Frontal), developing an automated protocol of network classification. Methods RS analysis has been carried out with CONN software [2] in 21 elderly subjects (11 M–10 F, 73 ± 4 years), acquired in GE 3T scanner with a EPI sequence (TR/TE 2000/30 ms, 3.2 × 3.2 × 3.4 mm3, Volumes 250): the raw fMRI images were spatially and temporally preprocessed with standard analysis steps and subsequently a Independent Component Analysis (ICA) was performed to carry out the RS networks for each subject. The four classes of relevant networks were tested against a class of random chosen networks in PRoNTo software [3] with a multiclass Gaussian Process. After the cross-validation test phase, the weight of single voxels into the classification process was obtained for each network class. Results PRoNTo successfully classified the networks with a permutation test obtaining a total accuracy of 92.38% (p Table 1 . Fig. 1 represents the confusion matrix. Conclusion The Machine Learning algorithm correctly identified four relevant RS networks against random ones, showing the possibility to automatize a protocol of RS network classification. Funding DRINN PROJECT – Diabetes Research Innovation 2015.

Default mode network connectivity of fear- and anxiety-related cue and context conditioning

Classical fear conditioning is an important mechanism to adequately respond and adapt to environmental threats and has been related to the development of fear and anxiety. Both cue and context conditioning have been studied but little is known about their relation to relevant resting state networks. The default mode network (DMN) has been reported to be involved in affective learning and described as facilitating a state of readiness in responding to environmental changes.We examined resting state brain connectivity patterns of the default mode network (DMN) in 119 healthy volunteers. Specifically, we carried out correlation analyses between the DMN and skin conductance responses (SCRs) as well as arousal, valence and contingency ratings during learning. In addition, we examined the role of trait anxiety. Two different DMN patterns were identified in which stronger connectivity was linked to lower differential SCRs during fear and anxiety learning. One was related to cue conditioning and involved the amygdala and the medial prefrontal cortex, and one was associated with context conditioning and included the hippocampal formation and sensorimotor areas. These results were replicated in an independent sample. Functional connectivity of the DMN with these key regions at rest was also predictive of trait anxiety but this association could not be replicated in the second sample.We showed that DMN connectivity is differently associated with cued versus contextual learning mechanisms. Uncovering individual differences in baseline network connectivity of the DMN with these key regions might lead to a better understanding of fear and anxiety. Such findings could indeed help to identify vulnerability factors linked to network alterations at rest with dysregulation of learning processes involved in the pathophysiology of stress and anxiety disorders.

Probing Intrinsic Resting-State Networks in the Infant Rat Brain.

Resting-state functional magnetic resonance imaging (rs-fMRI) measures spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signal in the absence of external stimuli. It has become a powerful tool for mapping large-scale brain networks in humans and animal models. Several rs-fMRI studies have been conducted in anesthetized and awake adult rats, reporting consistent patterns of brain activity at the systems level. However, the evolution to adult patterns of resting-state activity has not yet been evaluated and quantified in the developing rat brain. In this study, we hypothesized that large-scale intrinsic networks would be easily detectable but not fully established as specific patterns of activity in lightly anesthetized 2-week-old rats (N = 11). Independent component analysis (ICA) identified 8 networks in 2-week-old-rats. These included Default mode, Sensory (Exteroceptive), Salience (Interoceptive), Basal Ganglia-Thalamic-Hippocampal, Basal Ganglia, Autonomic, Cerebellar, as well as Thalamic-Brainstem networks. Many of these networks consisted of more than one component, possibly indicative of immature, underdeveloped networks at this early time point. Except for the Autonomic network, infant rat networks showed reduced connectivity with subcortical structures in comparison to previously published adult networks. Reported slow fluctuations in the BOLD signal that correspond to functionally relevant resting-state networks in 2-week-old rats can serve as an important tool for future studies of brain development in the settings of different pharmacological applications or disease.

Fitness, but not physical activity, is related to functional integrity of brain networks associated with aging

Greater physical activity and cardiorespiratory fitness are associated with reduced age-related cognitive decline and lower risk for dementia. However, significant gaps remain in the understanding of how physical activity and fitness protect the brain from adverse effects of brain aging. The primary goal of the current study was to empirically evaluate the independent relationships between physical activity and fitness with functional brain health among healthy older adults, as measured by the functional connectivity of cognitively and clinically relevant resting state networks. To build context for fitness and physical activity associations in older adults, we first demonstrate that young adults have greater within-network functional connectivity across a broad range of cortical association networks. Based on these results and previous research, we predicted that individual differences in fitness and physical activity would be most strongly associated with functional integrity of the networks most sensitive to aging. Consistent with this prediction, and extending on previous research, we showed that cardiorespiratory fitness has a positive relationship with functional connectivity of several cortical networks associated with age-related decline, and effects were strongest in the default mode network (DMN). Furthermore, our results suggest that the positive association of fitness with brain function can occur independent of habitual physical activity. Overall, our findings provide further support that cardiorespiratory fitness is an important factor in moderating the adverse effects of aging on cognitively and clinically relevant functional brain networks.

Altered resting state functional connectivity of anterior cingulate cortex in drug naïve adolescents at the earliest stages of anorexia nervosa.

Previous Resting-State Functional Connectivity (RSFC) studies have shown several functional alterations in adults with or recovered from long Anorexia Nervosa (AN). The aim of this paper was to investigate whole brain RSFC in adolescents with AN in the earliest stages, less than 6 months, of the disorder. Sixteen drug-naïve outpatient female adolescents with AN-restrictive type (AN-r) (mean age: 15,8; SD 1,7) were compared to 16 age-matched healthy female (mean age: 16,3; SD 1,4). Relevant resting state networks (RSNs) were identified using independent component analysis (ICA) from functional magnetic resonance imaging data; a dual regression technique was used to detect between-group differences in the RSNs. Between-group differences of the functional connectivity maps were found in the executive control network (ECN). Particularly, decreased temporal correlation was observed in AN-r patients relative to healthy controls between the ECN functional connectivity maps and the anterior cingulate cortex (p < 0.05 corrected). Our results in AN adolescents may represent an early trait-related biomarker of the disease. Considering that the above mentioned network and its area are mainly involved in cognitive control and emotional processing, our findings could explain the impaired cognitive flexibility in relation to body image and appetite in AN patients.

Differential relations between juvenile psychopathic traits and resting state network connectivity.

Traditionally, neurobiological research on psychopathy has focused on categorical differences in adults. However, there is evidence that psychopathy is best described by a set of relatively independent personality dimensions, that is, callous-unemotional, grandiose-manipulative, and impulsive-irresponsible traits, which can be reliably detected in juveniles, allowing investigation of the neural mechanisms leading to psychopathy. Furthermore, complex psychiatric disorders like psychopathy are increasingly being conceptualized as disorders of brain networks. The intrinsic organization of the brain in such networks is reflected by coherent fluctuations in resting state networks (RSNs), but these have not been studied in sufficient detail in relation to juvenile psychopathic traits yet. The current study investigated the distinct associations of juvenile psychopathic traits dimensions with RSN connectivity. Resting-state functional MRI and independent component analysis were used to assess RSN connectivity in a large sample of adolescents (n = 130, mean age 17.8 years) from a childhood arrestee cohort. Associations between scores on each of the three psychopathic traits dimensions and connectivity within and between relevant RSNs were investigated. Callous-unemotional traits were related to aberrant connectivity patterns of the default mode network, which has been implicated in self-referential and moral processes. Impulsive-irresponsible traits were associated with altered connectivity patterns in the frontoparietal cognitive control networks. Grandiose-manipulative traits were not associated with altered connectivity patterns. These findings confirm the association between psychopathic traits and brain network connectivity, and considerably add to emerging evidence supporting neurobiological heterogeneity in the processes leading to psychopathy.

Multivariate empirical mode decomposition based sub-frequency bands analysis of the default mode network: a resting-state fMRI data study

Abstract Resting-state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. The default mode network (DMN) is the most important and stable intrinsic connectivity network (ICN), which involves several cognition functions, such as episodic memory and self-introspection. It has been suggested that low-frequency fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain and these low-frequency fluctuations correspond to functionally relevant resting-state networks. Several studies have revealed that the function of the brain is accomplished in certain low sub-frequency band. However, the concerned frequency bands are determined by experience, neglecting the intrinsic information of BOLD time series. In this study, we apply a full data-driven analysis, i.e., multivariate empirical mode decomposition (MEMD), to decompose resting-state fMRI data into the different sub-band DMNs, aiming to reveal the corresponding connectivity functions in separate sub-band DMN. Our results revealed that MEMD can adaptively decompose signals into intrinsic mode functions (IMFs) with the similar patterns across subjects. Furthermore, the sub-network constructed from the IMFs revealed that the different sub-band DMNs correspond to the different brain functional connectivity, inferring the possible relationships between sub-frequency band and cognitions. Owing to its data-driven merit, the proposed MEMD analysis may provide a new insight for fMRI-related studies.

Computation of resting state networks from fMRI through a measure of phase synchrony.

Resting-state fMRI (rs-fMRI) studies of the human brain have demonstrated that low-frequency fluctuations can define functionally relevant resting state networks (RSNs). The majority of these methods rely on Pearson's correlation for quantifying the functional connectivity between the time series from different regions. However, it is well-known that correlation is limited to quantifying only linear relationships between the time series and assumes stationarity of the underlying processes. Many empirical studies indicate nonstationarity of the BOLD signals. In this paper, we adapt a measure of time-varying phase synchrony to quantify the functional connectivity and modify it to distinguish between synchronization and desynchronization. The proposed measure is compared to the conventional Pearson's correlation method for rs-fMRI analyses on two subjects (six scans per subject) in terms of their reproducibility.

Increased resting state functional connectivity in the default mode network in recovered anorexia nervosa

Functional brain imaging studies have shown abnormal neural activity in individuals recovered from anorexia nervosa (AN) during both cognitive and emotional task paradigms. It has been suggested that this abnormal activity which persists into recovery might underpin the neurobiology of the disorder and constitute a neural biomarker for AN. However, no study to date has assessed functional changes in neural networks in the absence of task-induced activity in those recovered from AN. Therefore, the aim of this study was to investigate whole brain resting state functional connectivity in nonmedicated women recovered from anorexia nervosa. Functional magnetic resonance imaging scans were obtained from 16 nonmedicated participants recovered from anorexia nervosa and 15 healthy control participants. Independent component analysis revealed functionally relevant resting state networks. Dual regression analysis revealed increased temporal correlation (coherence) in the default mode network (DMN) which is thought to be involved in self-referential processing. Specifically, compared to healthy control participants the recovered anorexia nervosa participants showed increased temporal coherence between the DMN and the precuneus and the dorsolateral prefrontal cortex/inferior frontal gyrus. The findings support the view that dysfunction in resting state functional connectivity in regions involved in self-referential processing and cognitive control might be a vulnerability marker for the development of anorexia nervosa.

Adolescent resting state networks and their associations to schizotypal trait expression

The rising interest in temporally coherent brain networks during baseline adult cerebral activity finds convergent evidence for an identifiable set of resting state networks (RSNs). To date, little is know concerning the earlier developmental stages of functional connectivity in RSNs. This study's main objective is to characterize the RSNs in a sample of adolescents. We further examine our data from a developmental psychopathology perspective of psychosis-proneness, by testing the hypothesis that early schizotypal symptoms are linked to disconnection in RSNs. In this perspective, this study examines the expression of adolescent schizotypal traits and their potential associations to dysfunctional RSNs. Thirty-nine adolescents aged between 12 and 20 years old underwent an 8-min functional magnetic resonance imaging (fMRI) “resting state” session. In order to explore schizotypal trait manifestations, the entire population was assessed by the Schizotypal Personality Questionnaire (SPQ). After conventional processing of the fMRI data, we applied group-level independent component analysis (ICA). Twenty ICA maps and associated time courses were obtained, among which there were RSNs that are consistent with findings in the literature. We applied a regression analysis at group level between the energy of RSN-associated time courses in different temporal frequency bins and the clinical measures (3 in total). Our results highlight the engagement of six relevant RSNs; (1) a default-mode network (DMN); (2) a dorso-lateral attention network; (3) a visual network (VN); (4) an auditory network (AN); (5) a sensory motor network (SMN); (6) a self-referential network (SRN). The regression analysis reveals a statistically significant correlation between the clinical measures and some of the RSNs, specifically the visual and the AN. In particular, a positive correlation is obtained for the VN in the low frequency range (0.05 Hz) with SPQ measures, while the AN correlates negatively in the high frequency range (0.16–0.19 Hz). Trend-like significance for the SRN may hint to its implication in disorganized thoughts and behaviors during adolescence. Unlike DMN activity in schizophrenic patients, adolescent DMN was unrelated to schizotypal trait expression. This suggests that relationships between the DMN and schizotypy may be modified in later developmental stages of both functional connectivity and psychotic expression. These results are discussed in light of RSNs literature involving children, adults, and individuals with schizophrenia.

Consistent resting-state networks across healthy subjects

Functional MRI (fMRI) can be applied to study the functional connectivity of the human brain. It has been suggested that fluctuations in the blood oxygenation level-dependent (BOLD) signal during rest reflect the neuronal baseline activity of the brain, representing the state of the human brain in the absence of goal-directed neuronal action and external input, and that these slow fluctuations correspond to functionally relevant resting-state networks. Several studies on resting fMRI have been conducted, reporting an apparent similarity between the identified patterns. The spatial consistency of these resting patterns, however, has not yet been evaluated and quantified. In this study, we apply a data analysis approach called tensor probabilistic independent component analysis to resting-state fMRI data to find coherencies that are consistent across subjects and sessions. We characterize and quantify the consistency of these effects by using a bootstrapping approach, and we estimate the BOLD amplitude modulation as well as the voxel-wise cross-subject variation. The analysis found 10 patterns with potential functional relevance, consisting of regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the so-called default-mode network, each with BOLD signal changes up to 3%. In general, areas with a high mean percentage BOLD signal are consistent and show the least variation around the mean. These findings show that the baseline activity of the brain is consistent across subjects exhibiting significant temporal dynamics, with percentage BOLD signal change comparable with the signal changes found in task-related experiments.

FMRI resting state networks define distinct modes of long-distance interactions in the human brain

Functional magnetic resonance imaging (fMRI) studies of the human brain have suggested that low-frequency fluctuations in resting fMRI data collected using blood oxygen level dependent (BOLD) contrast correspond to functionally relevant resting state networks (RSNs). Whether the fluctuations of resting fMRI signal in RSNs are a direct consequence of neocortical neuronal activity or are low-frequency artifacts due to other physiological processes (e.g., autonomically driven fluctuations in cerebral blood flow) is uncertain. In order to investigate further these fluctuations, we have characterized their spatial and temporal properties using probabilistic independent component analysis (PICA), a robust approach to RSN identification. Here, we provide evidence that: i. RSNs are not caused by signal artifacts due to low sampling rate (aliasing); ii. they are localized primarily to the cerebral cortex; iii. similar RSNs also can be identified in perfusion fMRI data; and iv. at least 5 distinct RSN patterns are reproducible across different subjects. The RSNs appear to reflect “default” interactions related to functional networks related to those recruited by specific types of cognitive processes. RSNs are a major source of non-modeled signal in BOLD fMRI data, so a full understanding of their dynamics will improve the interpretation of functional brain imaging studies more generally. Because RSNs reflect interactions in cognitively relevant functional networks, they offer a new approach to the characterization of state changes with pathology and the effects of drugs.