Intrinsic Brain Networks Research Articles

Intrinsic brain network alterations in non-clinical adults with a history of childhood trauma

ABSTRACT Background: Childhood trauma is a major social public-health problem worldwide. Previous literature suggests childhood trauma is associated with the development of psychiatric disorders and maladaptive behaviours later in life, but little is known about the neural basis underlying these associations. Objective: The aim of the current study was to investigate intrinsic brain network alterations in non-clinical adults with childhood trauma. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 65 non-clinical adults with moderate or severe childhood trauma (CT group), according to the international demarcation criteria of the Childhood Trauma Questionnaire (CTQ), and 73 socio-demographically matched non-clinical controls without childhood trauma (no-CT group). Independent component analysis (ICA) was used to extract subnetworks of the default mode network (DMN), salience network (SN), and central executive network (CEN). Results: ICA revealed that the CT group had increased FC of the left medial prefrontal cortex (mPFC) in the anterior DMN (aDMN), increased functional connectivity (FC) of the left anterior insula in the SN, and decreased FC of the inferior parietal gyrus of the right CEN (rCEN). Compared to the controls, the CT group had decreased inter-network FCs between the SN and posterior DMN (pDMN), as well as between the pDMN and rCEN. Conclusions: Impaired FC within the three key brain networks, decreased inter-FC between SN and rCEN, and decreased inter-FC between pDMN and rCEN may reflect biomarkers of childhood trauma.

Resting-state functional EEG connectivity in salience and default mode networks and their relationship to dissociative symptoms during NMDA receptor antagonism

N-methyl-d-aspartate receptor (NMDAR) antagonists administered to healthy humans results in schizophrenia-like symptoms, which are thought in part to be related to glutamatergically altered electrophysiological connectivity in large-scale intrinsic functional brain networks. Here, we examine resting-state source electroencephalographic (EEG) connectivity within and between the default mode (DMN: for self-related cognitive activity) and salience networks (SN: for detection of salient stimuli in internal and external environments) in 21 healthy volunteers administered a subanesthetic dose of the dissociative anesthetic and NMDAR antagonist, ketamine. In addition to provoking symptoms of dissociation, which are thought to originate from an altered sense of self that is common to schizophrenia, ketamine induces frequency-dependent increases and decreases in connectivity within and between DMN and SN. These altered interactive network couplings together with emergent dissociative symptoms tentatively support an NMDAR-hypofunction hypothesis of disturbed electrophysiologic connectivity in schizophrenia.

Individualized Prediction of PTSD Symptom Severity in Trauma Survivors From Whole-Brain Resting-State Functional Connectivity.

Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10–15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual’s functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual’s CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (r = 0.30, P = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic, and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks and highlights its potential application in making clinical evaluation of symptom severity at the individual level.

Parcels and particles: Markov blankets in the brain.

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions—and analyses—of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterizes undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organized criticality, dynamical instability, and so on. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems. Using the apparatus of the renormalization group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.

Disruption of concordance among spontaneous measures of intrinsic brain connectivity in Alzheimer’s disease

AbstractBackgroundIn the last years, several voxel‐wise data‐driven metrics gained attention to study resting‐state connectivity in healthy aging and various pathological states. More recently, a new measure assessing the interaction between the commonly used metrics has been proposed and made widely available via the DPABI toolbox: the so‐called concordance index (CI). This index reflects the coupling of different metrics. The value of this new measure in Alzheimer's disease (AD) research is yet unclear.MethodHealthy young controls (YC, n=26), healthy senior controls (SC, n=29), and patients diagnosed with AD (n=35) underwent 3T resting‐state fMRI. First, the fractional amplitude of low frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated. Next, the CI among these measures was determined for each time window and each brain voxel across time by using Kendall's W. Differences in the CI between groups were examined on a whole brain, voxel‐wise, and network‐level using masks of seven of the most prominent intrinsic brain networks, with age, sex, head motion and gray matter volume as covariates.ResultThe whole‐brain CI was significantly lower in AD patients as compared to YC and SC, which is in line with results from analyzing the single metrics. Reductions of the CI in AD was consistently observed in all networks investigated except for the limbic network. In SC compared to YC, no network‐level CI differences were detectable. On the voxel level, AD patients compared to SC showed lower CI in the right middle temporal gyrus, and SC compared to YC revealed lower CI in the left middle frontal gyrus. Analyzing the single metrics for their own, we observed a lower fALFF in the right angular gyrus in AD compared to SC, but no differences were detected for ReHo and DC.ConclusionThe newly proposed CI based on resting‐state connectivity may serve as a more sensitive imaging marker in AD than single metrics. The data suggest that this index distinguishes well between neurodegeneration and healthy aging.

The Self and Its Nature: A Psychopathological Perspective on the Risk-Reducing Effects of Environmental Green Space for Psychosis.

Epidemiological studies have shown that environmental green space contributes to the reduction of psychosis incidence in the population. Clarifying the psychological and neuro-functional mechanisms underlying the risk-decreasing effects of green surroundings could help optimize preventive environmental interventions. This perspective article specifically aims to open a new window on the link between environmental green space and psychosis by considering its core psychopathological features. Psychotic disorders, such as schizophrenia, are essentially characterized by self-disturbances. The psychological structure of the self has been described as a multidimensional phenomenon that emerges from the reciprocal interaction with the environment through intrinsic and extrinsic self-processes. The intrinsic self refers to the experience of mental activity and environmental information as inherently related to one’s own person, which involves self-referential processing, self-reflection, memory, interoception, and emotional evaluation. The extrinsic self refers to sensorimotor interactions with the environment and the sense of agency, that is, the experience of being the source of one’s own actions and the multisensory consequences thereof. In psychosis, anomalous self-processing has been related to a functional fragmentation of intrinsic and extrinsic self-processes and related brain networks. Moreover, evidence from cognitive neuroscience suggests that green space could have beneficial effects on self-related processing. Based on the literature, it could be hypothesized that self-processing is involved in mediating the beneficial effects of green space for psychosis. Considering the multidimensionality of the self, it is proposed that urban green space design aimed at improving mental health ideally impacts the complexity of self-facets and thus restores the individual’s self.

Connectivity Correlates of Anxiety Symptoms in Drug-Naive Parkinson's Disease Patients.

Anxiety symptoms are common in Parkinson's disease (PD). A link between anxiety and cognitive impairment in PD has been demonstrated. Using resting-state functional magnetic resonance imaging, we investigated intrinsic brain network connectivity correlates of anxiety symptoms in a cohort of drug-naive, cognitively unimpaired patients with PD. The intrinsic functional brain connectivity of 25 drug-naive, cognitively unimpaired PD patients with anxiety, 25 without anxiety, and 20 matched healthy controls was compared. All patients underwent a detailed behavioral and neuropsychological evaluation. Anxiety presence and severity were assessed using the Parkinson's Disease Anxiety Scale. Single-subject and group-level independent component analyses were used to investigate functional connectivity differences within and between the major resting-state networks. Decreased connectivity within the default-mode and sensorimotor networks (SMN), increased connectivity within the executive-control network (ECN), and divergent connectivity measures within salience and frontoparietal networks (SN and FPN) were detected in PD patients with anxiety compared with those without anxiety. Moreover, patients with anxiety showed a disrupted inter-network connectivity between SN and SMN, ECN, and FPN. Anxiety severity was correlated with functional abnormalities within these networks. Our findings demonstrated that an abnormal intrinsic connectivity within and between the most reported large-scale networks may represent a potential neural correlate of anxiety symptoms in drug-naive PD patients even in the absence of clinically relevant cognitive impairment. We hypothesize that these specific cognitive and limbic network architecture changes may represent a potential biomarker of treatment response in clinical trials. © 2020 International Parkinson and Movement Disorder Society.

Alterations in resting-state global brain connectivity in bipolar I disorder patients with prior suicide attempt.

Bipolar I disorder (BD-I) is associated with a high risk of suicide attempt; however, the neural circuit dysfunction that confers suicidal vulnerability in individuals with this disorder remains largely unknown. Resting-state functional magnetic resonance imaging (rs-fMRI) allows non-invasive mapping of brain functional connectivity. The current study used an unbiased voxel-based graph theory analysis of rs-fMRI to investigate the intrinsic brain networks of BD-I patients with and without suicide attempt. A total of 30 BD-I patients with suicide attempt (attempter group), 82 patients without suicide attempt (non-attempter group), and 67 healthy controls underwent rs-fMRI scan, and then global brain connectivity (GBC) was computed as the sum of connections of each voxel with all other gray matter voxels in the brain. Compared with the non-attempter group, we found regional differences in GBC values in emotion-encoding circuits, including the left superior temporal gyrus, bilateral insula/rolandic operculum, and right precuneus (PCu)/cuneus in the bipolar disorder (BD) attempter group, and these disrupted hub-like regions displayed fair to good power in distinguishing attempters from non-attempters among BD-I patients. GBC values of the right PCu/cuneus were positively correlated with illness duration and education in the attempter group. Our results indicate that abnormal connectivity patterns in emotion-encoding circuits are associated with the increasing risk of vulnerability to suicide attempt in BD patients, and global dysconnectivity across these emotion-encoding circuits might serve as potential biomarkers for classification of suicide attempt in BD patients.

Positive affect is inversely related to the salience and emotion network\u2019s connectivity

Increasing evidence has shown that positive affect enhances many aspects of daily functioning. Yet, how dispositional positive affect is represented in the intrinsic brain networks remains unclear. Here, we used resting-state functional Magnetic Resonance Imaging to test how trait positive and negative affect of an individual were associated with the intrinsic connectivity of brain regions within the salience and emotion network and the default mode network in 70 healthy young adults. We observed that positive affect was negatively associated with connectivity within the salience and emotion network, particularly with the bidirectional connections spanning the left anterior insula and left nucleus accumbens. For connections between the salience and emotion network and the rest of the brain, we observed that positive affect was negatively related to the connectivity between the right amygdala and the right middle temporal gyrus. Affect-based modulations of connectivity were specific to positive affect and to the salience and emotion network. Our findings highlight the critical role of salience and emotion network in the neural relations of positive affect, and lay the groundwork for future studies on modeling the connectivity of salience and emotion network to predict mental well-being.

Aberrant dynamic functional network connectivity in cirrhotic patients without overt hepatic encephalopathy

PurposeNeurocognitive impairment is a common complication in cirrhosis and is associated with alterations in static functional network connectivity (FNC) between distinct brain systems. However, accumulating evidence suggests temporal variability in FNC even at rest. This study aimed to explore dynamic FNC (dFNC) differences and to elucidate their association with neurocognitive changes in cirrhotic patients. MethodsFifty-four cirrhotic patients and 42 controls underwent resting-state functional magnetic resonance imaging. Psychometric hepatic encephalopathy score (PHES) was used to assess neurocognitive function. Independent component analysis was performed to identify the components of seven intrinsic brain networks, including sensorimotor (SMN), auditory, visual, cognitive control (CCN), default mode (DMN), subcortical (SC), and cerebellar networks. Sliding window correlation approach was employed to calculate dFNC. FNC states were determined by k-means clustering method, and then functional state analysis was conducted to measure dynamic indices. ResultsThe patients showed decreased dFNC in State 2, involving the connectivity between posterior subsystem of DMN and CCN (represented by bilateral insular cortex), and in State 3, involving the connectivity between SMN (represented by bilateral precentral gyrus) and SC (represented by bilateral putamen and caudate). The patients spent significantly longer time in State 4 that was with weakest FNC across all networks. We observed a significant correlation between PHES and fraction time/mean dwell time in State 4. ConclusionsAberrant dFNC may be the underlying mechanism of neurocognitive impairments in cirrhosis. Dynamic FNC analysis may potentially be utilized in investigating cirrhosis-related neuropathological processes.

Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders.

The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43years, IQ = 110.58; controls: n = 100, mean age = 11.43years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.

The Prospective Sense of Agency is Rooted in Local and Global Properties of Intrinsic Functional Brain Networks.

The sense of agency (SoA) refers to a constitutional aspect of the self describing the extent to which individuals feel in control over their actions and consequences thereof. Although the SoA has been associated with mental health and well-being, it is still unknown how interindividual variability in the SoA is embedded in the intrinsic brain organization. We hypothesized that the prospective component of an implicit SoA is associated with brain networks related to SoA and sensorimotor predictions on multiple spatial scales. We replicated previous findings by showing a significant prospective SoA as indicated by intentional binding effects. Then, using task-free fMRI and graph analysis, we analyzed associations between intentional binding effects and the intrinsic brain organization at regional, modular, and whole-brain scales. The results showed that intermodular connections of a frontoparietal module including the premotor cortex, supramarginal gyrus, and dorsal precuneus are associated with individual differences in prospective intentional binding. Notably, prospective intentional binding effects were also related to global brain modularity within a specific structural resolution range. These findings suggest that an implicit SoA generated through sensorimotor predictions relies on the intrinsic organization of the brain connectome on both local and global scales.

Temporal dynamics of resting EEG networks are associated with prosociality

As prosociality is key to facing many of our societies’ global challenges (such as fighting a global pandemic), we need to better understand why some individuals are more prosocial than others. The present study takes a neural trait approach, examining whether the temporal dynamics of resting EEG networks are associated with inter-individual differences in prosociality. In two experimental sessions, we collected 55 healthy males’ resting EEG, their self-reported prosocial concern and values, and their incentivized prosocial behavior across different reward domains (money, time) and social contexts (collective, individual). By means of EEG microstate analysis we identified the temporal coverage of four canonical resting networks (microstates A, B, C, and D) and their mutual communication in order to examine their association with an aggregated index of prosociality. Participants with a higher coverage of microstate A and more transitions from microstate C to A were more prosocial. Our study demonstrates that temporal dynamics of intrinsic brain networks can be linked to complex social behavior. On the basis of previous findings on links of microstate A with sensory processing, our findings suggest that participants with a tendency to engage in bottom-up processing during rest behave more prosocially than others.

Gyral-sulcal contrast in intrinsic functional brain networks across task performances

Functional mechanism of the brain and its relationship with the brain structural substrate have been an interest for multiple disciplines for centuries. Recently, gyri and sulci, two basic cortical folding patterns, have been demonstrated to act different functional roles. Specifically, a variety of functional MRI (fMRI) studies have consistently suggested that gyri represent a global functional center while sulci serve as a local functional unit under either resting state or task stimulus, which are further supported by brain structural analysis reporting that gyri have thicker cortex and denser long-distance axonal fibers. However, the consistency of such gyral-sulcal functional difference across different task stimuli, as well as its association with task conditions, remains to be explored. To this end, we used intrinsic networks as the testbed for cross-task comparison, and adopted a computational framework of dictionary learning and sparse representation of whole-brain fMRI signals to systematically examine the potential gyral-sulcal difference in signal representation residual (SRR) which reflected the degree of global functional communication. Using all seven task-based fMRI datasets in Human Connectome Project Q1 release, we found that within the intrinsic functional networks, the fMRI SRR was significantly smaller on gyral regions than on sulcal regions across different task stimuli, indicating that gyral regions were more involved in global functions of the brain and interregional communications. Moreover, the magnitudes of such gyral-sulcal difference varied across task conditions and intrinsic networks. Our work adds novel explanation and insight to the existing knowledge of functional differences between gyri and sulci.

Functional reorganization of brain networks across the human menstrual cycle

The brain is an endocrine organ, sensitive to the rhythmic changes in sex hormone production that occurs in most mammalian species. In rodents and nonhuman primates, estrogen and progesterone’s impact on the brain is evident across a range of spatiotemporal scales. Yet, the influence of sex hormones on the functional architecture of the human brain is largely unknown. In this dense-sampling, deep phenotyping study, we examine the extent to which endogenous fluctuations in sex hormones alter intrinsic brain networks at rest in a woman who underwent brain imaging and venipuncture for 30 consecutive days. Standardized regression analyses illustrate estrogen and progesterone’s widespread associations with functional connectivity. Time-lagged analyses examined the temporal directionality of these relationships and suggest that cortical network dynamics (particularly in the Default Mode and Dorsal Attention Networks, whose hubs are densely populated with estrogen receptors) are preceded—and perhaps driven—by hormonal fluctuations. A similar pattern of associations was observed in a follow-up study one year later. Together, these results reveal the rhythmic nature in which brain networks reorganize across the human menstrual cycle. Neuroimaging studies that densely sample the individual connectome have begun to transform our understanding of the brain’s functional organization. As these results indicate, taking endocrine factors into account is critical for fully understanding the intrinsic dynamics of the human brain.

Increased functional dynamics in civil aviation pilots: Evidence from a neuroimaging study.

Civil aviation is a distinctive career. Pilots need to monitor the entire system in real time. However, the psychophysiological mechanism of flying is largely unknown. The human brain is a large-scale interconnected organization, and many stable intrinsic large-scale brain networks have been identified. Among them are three core neurocognitive networks: default mode network (DMN), central executive network (CEN), and salience network (SN). These three networks play a critical role in human cognition. This study aims to examine the dynamic properties of the three large-scale brain networks in civil aviation pilots. We collected resting-state functional magnetic resonance imaging data from pilots. Independent component analysis, which is a data-driven approach, was combined with sliding window dynamic functional connectivity analysis to detect the dynamic properties of large-scale brain networks. Our results revealed that pilots exhibit an increased interaction of the CEN with the DMN and the SN along with a decreased interaction within the CEN. In addition, the temporal properties of functional dynamics (number of transitions) increased in pilots compared to healthy controls. In general, pilots exhibited increased between-network functional connectivity, decreased within-network functional connectivity, and a higher number of transitions. These findings suggest that pilots might have better functional dynamics and cognitive flexibility.

The Profiles of Non-stationarity and Non-linearity in the Time Series of Resting-State Brain Networks.

The linearity and stationarity of fMRI time series need to be understood due to their important roles in the choice of approach for brain network analysis. In this paper, we investigated the stationarity and linearity of resting-state fMRI (rs-fMRI) time-series data from the Midnight Scan Club datasets. The degree of stationarity (DS) and the degree of non-linearity (DN) were, respectively, estimated for the time series of all gray matter voxels. The similarity and difference between the DS and DN were assessed in terms of voxels and intrinsic brain networks, including the visual network, somatomotor network, dorsal attention network, ventral attention network, limbic network, frontoparietal network, and default-mode network. The test-retest scans were utilized to quantify the reliability of DS and DN. We found that DS and DN maps had overlapping spatial distribution. Meanwhile, the probability density estimate function of DS had a long tail, and that of DN had a more normal distribution. Specifically, stronger DS was present in the somatomotor, limbic, and ventral attention networks compared to other networks, and stronger DN was found in the somatomotor, visual, limbic, ventral attention, and default-mode networks. The percentage of overlapping voxels between DS and DN in different networks demonstrated a decreasing trend in the order default mode, ventral attention, somatomotor, frontoparietal, dorsal attention, visual, and limbic. Furthermore, the ICC values of DS were higher than those of DN. Our results suggest that different functional networks have distinct properties of non-stationarity and non-linearity owing to the complexity of rs-fMRI time series. Thus, caution should be taken when analyzing fMRI data (both resting-state and task-activation) using simplified models.

Frontoparietal and salience network alterations in obsessive–compulsive disorder: insights from independent component and sliding time window analyses

Resting-state functional MRI (fMRI) studies commonly report alterations in 3 core networks in obsessive–compulsive disorder (OCD) — the frontoparietal network, the default mode network and the salience network — defined by functionally connected infraslow oscillations in ongoing brain activity. However, most of these studies observed static functional connectivity in the brains of patients with OCD. To investigate dynamic functional connectivity alterations and widen the evidence base toward the triple network model in OCD, we performed group-based independent component and sliding time window analyses in 49 patients with OCD and 41 healthy controls. The traditional independent component analysis showed alterations in the left frontoparietal network as well as between the left and right frontoparietal networks in patients with OCD compared with healthy controls. For dynamic functional connectivity, the sliding time window approach revealed peak dysconnectivity between the left and right frontoparietal networks and between the left frontoparietal network and the salience network. The number of independent components, noise in the resting-state fMRI images, the heterogeneity of the OCD sample, and comorbidities and medication status in the patients could have biased the results. Disrupted modulation of these intrinsic brain networks may contribute to the pathophysiology of OCD.

Working memory load-dependent changes in cortical network connectivity estimated by machine learning

Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to characterize the interactions among cortical networks that are sensitive to working memory load since such interactions can also hint at the impaired dynamics in patients with poor working memory performance. Functional connectivity is a powerful tool used to investigate coordinated activity among local and distant brain regions. Here, we identified connectivity footprints that differentiate task states representing distinct working memory load levels. We employed linear support vector machines to decode working memory load from task-based functional connectivity matrices in 177 healthy adults. Using neighborhood component analysis, we also identified the most important connectivity pairs in classifying high and low working memory loads. We found that between-network coupling among frontoparietal, ventral attention and default mode networks, and within-network connectivity in ventral attention network are the most important factors in classifying low vs. high working memory load. Task-based within-network connectivity profiles at high working memory load in ventral attention and default mode networks were the most predictive of load-related increases in response times. Our findings reveal the large-scale impact of working memory load on the cerebral cortex and highlight the complex dynamics of intrinsic brain networks during active task states.

Large-scale network dysfunction in the acute state compared to the remitted state of bipolar disorder: A meta-analysis of resting-state functional connectivity.

BackgroundBipolar disorder (BD) is a mental disorder characterized by mood fluctuations between an acute episodic state of either mania or depression and a clinically remitted state. Dysfunction of large-scale intrinsic brain networks has been demonstrated in this disorder, but it remains unknown whether those network alterations are related to different states.MethodsIn the present study, we performed a meta-analysis of whole-brain seed-based resting-state functional connectivity (rsFC) studies in BD patients to compare the intrinsic function of brain networks between episodic and remitted states. Thirty-nine seed-based voxel-wise rsFC datasets from thirty publications (1047 BD patients vs 1081 controls) were included in the meta-analysis. Seeds were categorized into networks by their locations within a priori functional networks. Seed-based d mapping analysis of between-state effects identified brain systems in which different states were associated with increased connectivity or decreased connectivity within and between each seed network.FindingsWe found that BD patients presented decreased connectivity within the affective network (AN) in acute episodes but not in the remitted state of the illness. Similar decreased connectivity within the default-mode network (DMN) was also found in the acute state, but it was replaced by increased connectivity in the remitted state. In addition, different patterns of between-network dysconnectivity were observed between the acute and remitted states.InterpretationThis study is the first to identify different patterns of intrinsic function in large-scale brain networks between the acute and remitted states of BD through meta-analysis. The findings suggest that a shift in network function between the acute and remitted states may be related to distinct emotional and cognitive dysfunctions in BD, which may have important implications for identifying clinically relevant biomarkers to guide alternative treatment strategies for BD patients during active episodes or remission.FundingThis study was supported by grants from the National Natural Science Foundation of China (81171488, 81671669 and 81820108018) and by a Sichuan Provincial Youth Grant (2017JQ0001).