Large-scale Functional Connectivity Research Articles

Goal-Directed Processing of Naturalistic Stimuli Modulates Large-Scale Functional Connectivity.

Humans selectively process external information according to their internal goals. Previous studies have found that cortical activity and interactions between specific cortical areas such as frontal-parietal regions are modulated by behavioral goals. However, these results are largely based on simple stimuli and task rules in laboratory settings. Here, we investigated how top-down goals modulate whole-brain functional connectivity (FC) under naturalistic conditions. Analyses were conducted on a publicly available functional magnetic resonance imaging (fMRI) dataset (OpenfMRI database, accession number: ds000233) collected on twelve participants who made either behavioral or taxonomic judgments of behaving animals containing in naturalistic video clips. The task-evoked FC patterns of the participants were extracted using a novel inter-subject functional correlation (ISFC) method that increases the signal-to-noise ratio for detecting task-induced inter-regional correlation compared with standard FC analysis. Using multivariate pattern analysis (MVPA) methods, we successfully predicted the task goals of the participants with ISFC patterns but not with standard FC patterns, suggests that the ISFC method may be an efficient tool for exploring subtle network differences between brain states. We further examined the predictive power of several canonical brain networks and found that many within-network and across-network ISFC measures supported task goals classification. Our findings suggest that goal-directed processing of naturalistic stimuli systematically modulates large-scale brain networks but is not limited to the local neural activity or connectivity of specific regions.

Rapid Reconfiguration of the Functional Connectome after Chemogenetic Locus Coeruleus Activation

The locus coeruleus (LC) supplies norepinephrine (NE) to the entire forebrain, regulates many fundamental brain functions, and is implicated in several neuropsychiatric diseases. Although selective manipulation of the LC is not possible in humans, studies have suggested that strong LC activation might shift network connectivity to favor salience processing. To test this hypothesis, we use a mouse model to study the impact of LC stimulation on large-scale functional connectivity by combining chemogenetic activation of the LC with resting-state fMRI, an approach we term “chemo-connectomics”. LC activation rapidly interrupts ongoing behavior and strongly increases brain-wide connectivity, with the most profound effects in the salience and amygdala networks. We reveal a direct correlation between functional connectivity changes and transcript levels of alpha-1, alpha-2, and beta-1 adrenoceptors across the brain, and a positive correlation between NE turnover and functional connectivity within select brain regions. These results represent the first brain-wide functional connectivity mapping in response to LC activation, and demonstrate a causal link between receptor expression, brain states and functionally connected large-scale networks at rest. We propose that these changes in large-scale network connectivity are critical for optimizing neural processing in the context of increased vigilance and threat detection.

Differential intrinsic functional connectivity changes in semantic variant primary progressive aphasia.

The semantic variant of primary progressive aphasia (svPPA) is a clinical syndrome characterized by semantic memory deficits with relatively preserved motor speech, syntax, and phonology. There is consistent evidence linking focal neurodegeneration of the anterior temporal lobes (ATL) to the semantic deficits observed in svPPA. Less is known about large-scale functional connectivity changes in this syndrome, particularly regarding the interplay between affected and spared language networks that leads to the unique cognitive dissociations typical of svPPA.Using whole-brain, seed-based connectivity on task-free Magnetic Resonance Imaging (MRI) data, we studied connectivity of networks anchored to three left-hemisphere regions crucially involved in svPPA symptomatology: ATL just posterior to the main atrophic area, opercular inferior frontal gyrus, and posterior inferior temporal lobe. First, in 32 healthy controls, these seeds isolated three networks: a ventral semantic network involving anterior middle temporal and angular gyri, a dorsal articulatory-phonological system involving inferior frontal and supramarginal regions, and a third functional connection between posterior inferior temporal and intraparietal regions likely involved in linking visual and linguistic processes. We then compared connectivity strength of these three networks between 16 svPPA patients and the 32 controls. In svPPA, decreased functional connectivity in the ventral semantic network correlated with weak semantic skills, while connectivity of the network seeded from the posterior inferior temporal lobe, though not significantly different between the two groups, correlated with pseudoword reading skills. Increased connectivity between the inferior frontal gyrus and the superior portion of the angular gyrus suggested possible adaptive changes.Our findings have two main implications. First, they support a functional subdivision of the left IPL based on its connectivity to specific language-related regions. Second, the unique neuroanatomical and linguistic profile observed in svPPA provides a compelling model for the functional interplay of these networks, being either up- or down- regulated in response to disease.

Local thalamic atrophy associates with large-scale functional connectivity alterations of fronto-parietal cortices in genetic generalized epilepsies

Genetic generalized epilepsies (GGEs) are a group of seizure syndromes that start in childhood and adolescence. Although generally viewed as benign, large-scale epidemiological studies suggest that a significant proportion of GGE patients suffer from drug-resistant seizures, cognitive impairment and social problems. This motivates further research into their pathophysiology, which is still incompletely understood. GGE is characterized clinically and on the encephalogram by seizures that seem to involve both hemispheres simultaneously – hence the idea of a ‘generalized’ process. However, findings from experimental animal studies suggest that seizures in GGE arise due to complex functional alterations within a network that involves fronto-parietal cortex and midline thalamus. In line with these results, neuroimaging studies have found metabolic changes in midline frontal and posterior parietal cortices during GGE seizures and atrophy of both frontal lobe structures and thalamus in GGE patients. Pathology of fronto-thalamic networks seems therefore to be a core feature of GGE. It is unknown how alterations of structure and function between different sites of the network influence each other. Given that the thalamus exerts widespread influence on cortical function, we hypothesized that thalamic atrophy in GGE patients would lead to functional impairment in cortical networks. To test this hypothesis, we performed a case–control study on patients with GGE and healthy controls (HCs), using computational neuroanatomical and functional connectivity techniques. Confirming our hypothesis, we found atrophy in midline thalamic regions preferentially connected to midline (pre-) frontal cortex, and correlated functional disconnection between midline frontal and posterior parietal cortex. Of note, we found increased functional connectivity between the left-sided thalamus and the left medial prefrontal cortex, and a decrease in interhemispheric functional connectivity between bilateral parietal cortex in patients compared to HCs. Taken together, our results suggest that even highly localized subcortical structural changes might lead to large-scale network effects in GGE.

Subject specificity of the correlation between large-scale structural and functional connectivity.

Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual’s SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.

Differences in Large-scale and Sliding-window-based Functional Networks of Reappraisal and Suppression

The process model of emotion regulation suggests that cognitive reappraisal and expressive suppression engage at different time points in the regulation process. Although multiple brain regions and networks have been identified for each strategy, no articles have explored changes in network characteristics or network connectivity over time. The present study examined (a) the whole-brain network and six other resting-state networks, (b) their modularity and global efficiency, which is an index of the efficiency of information exchange across the network, (c) the degree and betweenness centrality for 160 brain regions to identify the hub nodes with the most control over the entire network, and (d) the intra-network and inter-network functional connectivity (FC). Such investigations were performed using a traditional large-scale FC analysis and a relatively recent sliding window correlation analysis. The results showed that the right inferior orbitofrontal cortex was the hub region of the whole-brain network for both strategies. The present findings of temporally altering functional activity of the networks revealed that the default mode network (DMN) activated at the early stage of reappraisal, followed by the task-positive networks (cingulo-opercular network and fronto-parietal network), emotion-processing networks (the cerebellar network and DMN), and sensorimotor network (SMN) that activated at the early stage of suppression, followed by the greater recruitment of task-positive networks and their functional connection with the emotional response-related networks (SMN and occipital network). This is the first study that provides neuroimaging evidence supporting the process model of emotion regulation by revealing the temporally varying network efficiency and intra- and inter-network functional connections of reappraisal and suppression.

Image categorization from functional magnetic resonance imaging using functional connectivity

BackgroundPrevious studies have attempted to infer the category of objects in a stimulus image from functional magnetic resonance imaging (fMRI) data recoded during image-viewing. Most studies focus on extracting activity patterns within a given region or across multiple voxels, and utilize the relationships among voxels to decipher the category of a stimulus image. Yet, the functional connectivity (FC) patterns across regions of interest in response to image categories, and their potential contributions to category classification are largely unknown. New methodWe investigated whole-brain FC patterns in response to 4 image category stimuli (cats, faces, houses, and vehicles) using fMRI in healthy adult volunteers, and classified FC patterns using machine learning framework (Support Vector Machine [SVM] and Random Forest). We further examined the FC robustness and the influence of the window length on FC patterns for neural decoding. ResultsThe average one-vs.-one classification accuracy of the two classification models were 74% within subjects and 80% between subjects, which are higher than the chance level (50%). The Random Forest results were better than SVM results, and the 48-s FC results were better than the 24-s FC results. Comparison with existing method(s)We compared the classification performance of our FC patterns with two other existing methods, inter-block and intra-block, without overlapping temporal information. ConclusionsWhole-brain FC patterns for different window lengths (24 and 48 s) can predict images categories with high accuracy. These results reveal novel mechanisms underlying the representation of categorical information in large-scale FC patterns in the human brain.

Comparing the temporal relationship of structural and functional connectivity changes in different adult human brain networks: a single-case study

Background: Despite accumulated evidence for adult brain plasticity, the temporal relationships between large-scale functional and structural connectivity changes in human brain networks remain unclear. Methods: By analysing a unique richly detailed 19-week longitudinal neuroimaging dataset, we tested whether macroscopic functional connectivity changes lead to the corresponding structural alterations in the adult human brain, and examined whether such time lags between functional and structural connectivity changes are affected by functional differences between different large-scale brain networks. Results: In this single-case study, we report that, compared to attention-related networks, functional connectivity changes in default-mode, fronto-parietal, and sensory-related networks occurred in advance of modulations of the corresponding structural connectivity with significantly longer time lags. In particular, the longest time lags were observed in sensory-related networks. In contrast, such significant temporal differences in connectivity change were not seen in comparisons between anatomically categorised different brain areas, such as frontal and occipital lobes. These observations survived even after multiple validation analyses using different connectivity definitions or using parts of the datasets. Conclusions: Although the current findings should be examined in independent datasets with different demographic background and by experimental manipulation, this single-case study indicates the possibility that plasticity of macroscopic brain networks could be affected by cognitive and perceptual functions implemented in the networks, and implies a hierarchy in the plasticity of functionally different brain systems.

Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

Objective. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. Approach. The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. Main results. We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. Significance. Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

One Size Does Not Fit All: Large-scale neuronal network Functional Connectivity differs across Cognitive Phenotype groups in Multiple Sclerosis (P3.380)

One Size Does Not Fit All: Large-scale neuronal network Functional Connectivity differs across Cognitive Phenotype groups in Multiple Sclerosis (P3.380)

Multivariate Pattern Classification of Facial Expressions Based on Large-Scale Functional Connectivity.

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA). We employed a block design experiment and collected neural activities while participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise). Both static and dynamic expression stimuli were included in our study. A behavioral experiment after scanning confirmed the validity of the facial stimuli presented during the fMRI experiment with classification accuracies and emotional intensities. We obtained whole-brain FC patterns for each facial expression and found that both static and dynamic facial expressions could be successfully decoded from the FC patterns. Moreover, we identified the expression-discriminative networks for the static and dynamic facial expressions, which span beyond the conventional face-selective areas. Overall, these results reveal that large-scale FC patterns may also contain rich expression information to accurately decode facial expressions, suggesting a novel mechanism, which includes general interactions between distributed brain regions, and that contributes to the human facial expression recognition.

Altered task-dependent functional connectivity patterns during subjective recollection experiences of episodic retrieval in postpartum women.

Numerous studies have suggested that postpartum women show a decline in cognitive abilities. However, to date, no study has investigated the presence of qualitative alterations in recognition memory processes in postpartum women that may lead to a decline in cognitive ability. To address this issue, we employed the Remember/Know procedure and functional magnetic resonance imaging (fMRI). Behavioral results demonstrated that compared with the matched control (CTRL) group, the postpartum (PP) group endorsed "Remember" less and "Know" more to old items. A univariate analysis of fMRI data indicated lower neural activity of the subjective recollection network in the PP group than in the CTRL group. We also performed a large-scale functional connectivity multivariate pattern analysis (fcMVPA) using task-dependent time-series to detect differences in functional connectivity patterns and neural interactivity between the PP and CTRL groups. The fcMVPA results revealed that the PP group exhibited altered functional connectivity patterns from which machine learning algorithms could discriminate group membership with 94% accuracy. Collectively, these findings demonstrated that altered subjective recollection processes in the PP group during episodic memory decisions are associated with diminished neural activity and abnormal interactivity across the subjective recollection network. We believe that this is one of the first studies demonstrating qualitative alterations in recognition memory processes in postpartum women.

Commentary: Novelty seeking and reward dependence-related large-scale brain networks functional connectivity variation during salience expectancy.

Commentary: Novelty seeking and reward dependence-related large-scale brain networks functional connectivity variation during salience expectancy.

Emotion Representation in Whole-brain Functional Connectivity Patterns: An fMRI Study

The representation of emotion in human brain is an important question in the cognitive neuroscience. However, it was remained unclear to what extent the connections between different brain regions contribute to the emotion recognition. The present study mainly focused on the emotion decoding based on the functional connectivity patterns. We designed the experiment and collected the neural activities while participants viewed emotion stimuli using the functional magnetic resonance imaging (fMRI) technology. We constructed the whole-brain functional connectivity patterns for each emotion, and performed emotion classification using multivariate pattern analysis combined with machine learning algorithms. We found that emotions could be successfully decoded from the whole-brain functional connectivity patterns. These results provide new evidence that large-scale functional connectivity patterns contain rich emotion information and contribute to the emotion recognition. Our study extends exist fMRI studies on emotion perception and may further our understanding of how human beings achieve easy and quick recognition of emotions.

Vagal nerve stimulation triggers widespread responses and alters large-scale functional connectivity in the rat brain.

Vagus nerve stimulation (VNS) is a therapy for epilepsy and depression. However, its efficacy varies and its mechanism remains unclear. Prior studies have used functional magnetic resonance imaging (fMRI) to map brain activations with VNS in human brains, but have reported inconsistent findings. The source of inconsistency is likely attributable to the complex temporal characteristics of VNS-evoked fMRI responses that cannot be fully explained by simplified response models in the conventional model-based analysis for activation mapping. To address this issue, we acquired 7-Tesla blood oxygenation level dependent fMRI data from anesthetized Sprague–Dawley rats receiving electrical stimulation at the left cervical vagus nerve. Using spatially independent component analysis, we identified 20 functional brain networks and detected the network-wise activations with VNS in a data-driven manner. Our results showed that VNS activated 15 out of 20 brain networks, and the activated regions covered >76% of the brain volume. The time course of the evoked response was complex and distinct across regions and networks. In addition, VNS altered the strengths and patterns of correlations among brain networks relative to those in the resting state. The most notable changes in network-network interactions were related to the limbic system. Together, such profound and widespread effects of VNS may underlie its unique potential for a wide range of therapeutics to relieve central or peripheral conditions.

Disrupted architecture of large-scale brain functional connectivity networks in patients with generalized tonic\u2013clonic seizure

Generalized tonic–clonic seizure (GTCS) is characterized by the abnormal functional organization among distant brain regions. Previous studies in GTCS that have comprehensively examined connectivity abnormalities across the complete range of large-scale brain networks remain relatively rare. Here, we employed an amount of regions of interest to investigate the intra- and inter-connections among seven large-scale brain networks in GTCS and healthy controls. Network contingency analysis revealed that patients with GTCS exhibit significantly increased connectivity between default mode network (DMN) and frontoparietal network (FPN), between DMN and dorsal attention network, and between somatomotor network and limbic network, and decreased functional connectivity within FPN (all p values were Bonferroni corrected). Consistent with existing evidence, the disrupted functional architecture of the DMN and task-positive network may be related to self-related processes and deficits in cognitive control and attention in patients. These findings support the notion that GTCS is associated with disrupted architecture in large-scale brain networks, providing information for better understanding of the pathophysiological mechanisms of GTCS.

Transcranial electric stimulation (tES) to early visual areas alters large-scale functional connectivity.

Transcranial electric stimulation (tES) to early visual areas alters large-scale functional connectivity.

Dynamic reconfiguration of cortical functional connectivity across brain states

Throughout each day, the brain displays transient changes in state, as evidenced by shifts in behavior and vigilance. While the electrophysiological correlates of brain states have been studied for some time, it remains unclear how large-scale cortico-cortical functional connectivity systematically reconfigures across states. Here, we investigate state-dependent shifts in cortical functional connectivity by recording local field potentials (LFPs) during spontaneous behavioral transitions in the ferret using chronically implanted micro-electrocorticographic (µECoG) arrays positioned over occipital, parietal, and temporal cortical regions. To objectively classify brain state, we describe a data-driven approach that projects time-varying LFP spectral properties into brain state space. Distinct brain states displayed markedly different patterns of cross-frequency phase-amplitude coupling and inter-electrode phase synchronization across several LFP frequency bands. The largest across-state differences in functional connectivity were observed between periods of presumed slow-wave and rapid-eye-movement-sleep/active-state, which were characterized by the contrasting phenomena of cortical network fragmentation and global synchronization, respectively. Collectively, our data provide strong evidence that large-scale functional interactions in the brain dynamically reconfigure across behavioral states.

Structural Basis of Large-Scale Functional Connectivity in the Mouse.

Translational neuroimaging requires approaches and techniques that can bridge between multiple different species and disease states. One candidate method that offers insights into the brain's functional connectivity (FC) is resting-state fMRI (rs-fMRI). In both humans and nonhuman primates, patterns of FC (often referred to as the functional connectome) have been related to the underlying structural connectivity (SC; also called the structural connectome). Given the recent rise in preclinical neuroimaging of mouse models, it is an important question whether the mouse functional connectome conforms to the underlying SC. Here, we compared FC derived from rs-fMRI in female mice with the underlying monosynaptic structural connectome as provided by the Allen Brain Connectivity Atlas. We show that FC between interhemispheric homotopic cortical and hippocampal areas, as well as in cortico-striatal pathways, emerges primarily via monosynaptic structural connections. In particular, we demonstrate that the striatum (STR) can be segregated according to differential rs-fMRI connectivity patterns that mirror monosynaptic connectivity with isocortex. In contrast, for certain subcortical networks, FC emerges along polysynaptic pathways as shown for left and right STR, which do not share direct anatomical connections, but high FC is putatively driven by a top-down cortical control. Finally, we show that FC involving cortico-thalamic pathways is limited, possibly confounded by the effect of anesthesia, small regional size, and tracer injection volume. These findings provide a critical foundation for using rs-fMRI connectivity as a translational tool to study complex brain circuitry interactions and their pathology due to neurological or psychiatric diseases across species.SIGNIFICANCE STATEMENT A comprehensive understanding of how the anatomical architecture of the brain, often referred to as the "connectome," corresponds to its function is arguably one of the biggest challenges for understanding the brain and its pathologies. Here, we use the mouse as a model for comparing functional connectivity (FC) derived from resting-state fMRI with gold standard structural connectivity measures based on tracer injections. In particular, we demonstrate high correspondence between FC measurements of cortico-cortical and cortico-striatal regions and their anatomical underpinnings. This work provides a critical foundation for studying the pathology of these circuits across mouse models and human patients.

Mind-Body Practice Changes Fractional Amplitude of Low Frequency Fluctuations in Intrinsic Control Networks.

Cognitive control impairment is a typical symptom largely reported in populations with neurological disorders. Previous studies have provided evidence about the changes in cognitive control induced by mind-body training. However, the neural correlates underlying the effect of extensive mind-body practice on cognitive control remain largely unknown. Using resting-state functional magnetic resonance imaging, we characterized dynamic fluctuations in large-scale intrinsic connectivity networks associated with mind-body practice, and examined their differences between healthy controls and Tai Chi Chuan (TCC) practitioners. Compared with a control group, the TCC group revealed significantly decreased fractional Amplitude of Low Frequency Fluctuations (fALFF) in the bilateral frontoparietal network, default mode network and dorsal prefrontal-angular gyri network. Furthermore, we detected a significant association between mind-body practice experience and fALFF in the default mode network, as well as an association between cognitive control performance and fALFF of the frontoparietal network. This provides the first evidence of large-scale functional connectivity in brain networks associated with mind-body practice, shedding light on the neural network changes that accompany intensive mind-body training. It also highlights the functionally plastic role of the frontoparietal network in the context of the “immune system” of mental health recently developed in relation to flexible hub theory.