The nature of the self: Neural analyses and heritability estimates of self‐evaluations in middle childhood

How neural correlates of self‐concept are influenced by environmental versus genetic factors is currently not fully understood. We investigated heritability estimates of behavioral and neural correlates of self‐concept in middle childhood since this phase is an important time window for taking on new social roles in academic and social contexts. To do so, a validated self‐concept fMRI task was applied in a twin sample of 345 participants aged between 7 and 9 years. In the self‐concept condition, participants were asked to indicate whether academic and social traits applied to them whereas the control condition required trait categorization. The self‐processing activation analyses (n = 234) revealed stronger medial prefrontal cortex (mPFC) activation for self than for control conditions. This effect was more pronounced for social‐self than academic self‐traits, whereas stronger dorsolateral prefrontal cortex (DLPFC) activation was observed for academic versus social self‐evaluations. Behavioral genetic modeling (166 complete twin pairs) revealed that 25–52% of the variation in academic self‐evaluations was explained by genetic factors, whereas 16–49% of the variation in social self‐evaluations was explained by shared environmental factors. Neural genetic modeling (91 complete twin pairs) for variation in mPFC and anterior prefrontal cortex (PFC) activation for academic self‐evaluations confirmed genetic and unique environmental influences, whereas anterior PFC activation for social self‐evaluations was additionally influenced by shared environmental influences. This indicates that environmental context possibly has a larger impact on the behavioral and neural correlates of social self‐concept at a young age. This is the first study demonstrating in a young twin sample that self‐concept depends on both genetic and environmental factors, depending on the specific domain.     

Do self‐reports of procrastination predict actual behavior?

ObjectivesProcrastination is typically assessed via self‐report questionnaires. So far, only very few studies have examined actual procrastination behavior, providing inconclusive results regarding the real‐life validity of self‐reports in this domain. The present study aimed to examine for the first time whether participants'' self‐reported procrastination can predict their actual behavior on a real‐life task.MethodsFor that purpose, we assessed self‐reported levels of procrastination [via the Pure Procrastination Scale, PPS] and actual procrastination behavior on a naturalistic task [i.e., having to send in an attendance sheet before a deadline] in 93 participants.ResultsResults show that self‐reports significantly predicted procrastination behavior. Analyses of underlying dimensions suggest that real‐life procrastination can be the result of “voluntarily delaying planned actions,” but can also have more passive causes such as “running out of time.”ConclusionsComparing our results with the available literature suggests that PPS self‐reports reflect a particularly valid tool to assess real‐life procrastination behavior. Findings are discussed in the context of strategies and mechanisms that potential interventions may target in order to reduce procrastination.     

Differences in working memory coding of biological motion attributed to oneself and others

The question how the brain distinguishes between information about self and others is of fundamental interest to both philosophy and neuroscience. In this functional magnetic resonance imaging (fMRI) study, we sought to distinguish the neural substrates of representing a full‐body movement as one''s movement and as someone else''s movement. Participants performed a delayed match‐to‐sample working memory task where a retained full‐body movement (displayed using point‐light walkers) was arbitrarily labeled as one''s own movement or as performed by someone else. By using arbitrary associations we aimed to address a limitation of previous studies, namely that our own movements are more familiar to us than movements of other people. A searchlight multivariate decoding analysis was used to test where information about types of movement and about self‐association was coded. Movement specific activation patterns were found in a network of regions also involved in perceptual processing of movement stimuli, however not in early sensory regions. Information about whether a memorized movement was associated with the self or with another person was found to be coded by activity in the left middle frontal gyrus (MFG), left inferior frontal gyrus (IFG), bilateral supplementary motor area, and (at reduced threshold) in the left temporoparietal junction (TPJ). These areas are frequently reported as involved in action understanding (IFG, MFG) and domain‐general self/other distinction (TPJ). Finally, in univariate analysis we found that selecting a self‐associated movement for retention was related to increased activity in the ventral medial prefrontal cortex.     

Role of anterior midcingulate cortex in self‐reward representation and reward allocation judgments within social context

Evaluating rewards for the self and others is essential for social interactions. Previous research has probed the neural substrates signaling rewards in social decision‐making tasks as well as the differentiation between self‐ and other‐reward representations. However, studies with different designs have yielded mixed results. After analyzing and comparing previous designs, we differentiated three components in this study: task (reward representation vs. social judgment of reward allocation), agency (self vs. other), and social context (without vs. within). Participants were asked to imagine various share sizes as a proposer in a dictator game during fMRI, and then rated their willingness and preference for these offers in a post‐scan behavioral task. To differentiate the regions involved in processing rewards without and within context, we presented the reward to each agent in two sequential frames. Parametric analyses showed that, in the second frame (i.e., within social context), the anterior midcingulate cortex (aMCC) signaled self‐reward and preferences for the offer, whereas the right insula tracked the likelihood of proposing the offer. Belief in a just world is positively associated with aMCC responses to self‐reward. These results shed light on the role of the aMCC in coding self‐reward within the social context to guide social behaviors.     

The anxiety‐specific hippocampus–prefrontal cortex pathways links to procrastination through self‐control

Procrastination, which is defined as delaying an intended course of action despite negative outcomes, is demonstrated to have a deal with negative emotion including trait anxiety. Although highly anxious individuals showed impoverished control ability, no studies have indicated the role of self‐control in the relationship between trait anxiety and procrastination, and its neural correlates. To this end, we used the sliding window method to calculate the temporal deviation of dynamic functional connectivity (FC) in 312 healthy participants who underwent the resting‐state functional magnetic resonance imaging (fMRI) scanning. In line with our hypothesis, higher trait anxiety is linked to more procrastination via poorer self‐control. Besides, the dynamic FC analyses showed that trait anxiety was positively correlated with dynamic FC variability in hippocampus–prefrontal cortex (HPC–PFC) pathways, including left rostral hippocampus–left superior frontal gyrus (left rHPC–left SFG), and left rHPC–right middle frontal gyrus (left rHPC–‐MFG). Furthermore, the structural equation modeling (SEM) uncovered a mediated role of self‐control in the association between the anxiety‐specific brain connectivity and procrastination. These findings suggest that the HPC–PFC pathways may reflect impoverished regulatory ability over the negative thoughts for anxious individuals, and thereby incurs more procrastination, which enhances our understanding of how trait anxiety links to procrastination.     

Evaluation of denoising strategies for task‐based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks

In‐scanner head motion represents a major confounding factor in functional connectivity studies and it raises particular concerns when motion correlates with the effect of interest. One such instance regards research focused on functional connectivity modulations induced by sustained cognitively demanding tasks. Indeed, cognitive engagement is generally associated with substantially lower in‐scanner movement compared with unconstrained, or minimally constrained, conditions. Consequently, the reliability of condition‐dependent changes in functional connectivity relies on effective denoising strategies. In this study, we evaluated the ability of common denoising pipelines to minimize and balance residual motion‐related artifacts between resting‐state and task conditions. Denoising pipelines—including realignment/tissue‐based regression, PCA/ICA‐based methods (aCompCor and ICA‐AROMA, respectively), global signal regression, and censoring of motion‐contaminated volumes—were evaluated according to a set of benchmarks designed to assess either residual artifacts or network identifiability. We found a marked heterogeneity in pipeline performance, with many approaches showing a differential efficacy between rest and task conditions. The most effective approaches included aCompCor, optimized to increase the noise prediction power of the extracted confounding signals, and global signal regression, although both strategies performed poorly in mitigating the spurious distance‐dependent association between motion and connectivity. Censoring was the only approach that substantially reduced distance‐dependent artifacts, yet this came at the great cost of reduced network identifiability. The implications of these findings for best practice in denoising task‐based functional connectivity data, and more generally for resting‐state data, are discussed.     

Decoding motor expertise from fine‐tuned oscillatory network organization

Can motor expertise be robustly predicted by the organization of frequency‐specific oscillatory brain networks? To answer this question, we recorded high‐density electroencephalography (EEG) in expert Tango dancers and naïves while viewing and judging the correctness of Tango‐specific movements and during resting. We calculated task‐related and resting‐state connectivity at different frequency‐bands capturing task performance (delta [δ], 1.5–4 Hz), error monitoring (theta [θ], 4–8 Hz), and sensorimotor experience (mu [μ], 8–13 Hz), and derived topographical features using graph analysis. These features, together with canonical expertise measures (i.e., performance in action discrimination, time spent dancing Tango), were fed into a data‐driven computational learning analysis to test whether behavioral and brain signatures robustly classified individuals depending on their expertise level. Unsurprisingly, behavioral measures showed optimal classification (100%) between dancers and naïves. When considering brain models, the task‐based classification performed well (~73%), with maximal discrimination afforded by theta‐band connectivity, a hallmark signature of error processing. Interestingly, mu connectivity during rest outperformed (100%) the task‐based approach, matching the optimal classification of behavioral measures and thus emerging as a potential trait‐like marker of sensorimotor network tuning by intense training. Overall, our findings underscore the power of fine‐tuned oscillatory network signatures for capturing expertise‐related differences and their potential value in the neuroprognosis of learning outcomes.     

The dual nature of the BOLD signal: Responses in visual area hMT+ reflect both input properties and perceptual decision

Neuroimaging studies have suggested that hMT+ encodes global motion interpretation, but this contradicts the notion that BOLD activity mainly reflects neuronal input. While measuring fMRI responses at 7 Tesla, we used an ambiguous moving stimulus, yielding the perception of two incoherently moving surfaces—component motion—or only one coherently moving surface—pattern motion, to induce perceptual fluctuations and identify perceptual organization size‐matched domains in hMT+. Then, moving gratings, exactly matching either the direction of component or pattern motion percepts of the ambiguous stimulus, were shown to the participants to investigate whether response properties reflect the input or decision. If hMT+ responses reflect the input, component motion domains (selective to incoherent percept) should show grating direction stimulus‐dependent changes, unlike pattern motion domains (selective to the coherent percept). This hypothesis is based on the known direction‐selective nature of inputs in component motion perceptual domains versus non‐selectivity in pattern motion perceptual domains. The response amplitude of pattern motion domains did not change with grating direction (consistently with their non‐selective input), in contrast to what happened for the component motion domains (consistently with their selective input). However, when we analyzed relative ratio measures they mirrored perceptual interpretation. These findings are consistent with the notion that patterns of BOLD responses reflect both sensory input and perceptual read‐out.     

Updating the dual‐mechanism model for cross‐sensory attentional spreading: The influence of space‐based visual selective attention

Selective attention to visual stimuli can spread cross‐modally to task‐irrelevant auditory stimuli through either the stimulus‐driven binding mechanism or the representation‐driven priming mechanism. The stimulus‐driven attentional spreading occurs whenever a task‐irrelevant sound is delivered simultaneously with a spatially attended visual stimulus, whereas the representation‐driven attentional spreading occurs only when the object representation of the sound is congruent with that of the to‐be‐attended visual object. The current study recorded event‐related potentials in a space‐selective visual object‐recognition task to examine the exact roles of space‐based visual selective attention in both the stimulus‐driven and representation‐driven cross‐modal attentional spreading, which remain controversial in the literature. Our results yielded that the representation‐driven auditory Nd component (200–400 ms after sound onset) did not differ according to whether the peripheral visual representations of audiovisual target objects were spatially attended or not, but was decreased when the auditory representations of target objects were presented alone. In contrast, the stimulus‐driven auditory Nd component (200–300 ms) was decreased but still prominent when the peripheral visual constituents of audiovisual nontarget objects were spatially unattended. These findings demonstrate not only that the representation‐driven attentional spreading is independent of space‐based visual selective attention and benefits in an all‐or‐nothing manner from object‐based visual selection for actually presented visual representations of target objects, but also that although the stimulus‐driven attentional spreading is modulated by space‐based visual selective attention, attending to visual modality per se is more likely to be the endogenous determinant of the stimulus‐driven attentional spreading.     

The effect of conscientiousness on procrastination: The interaction between the self‐control and motivation neural pathways

Procrastination is a prevalent and universal problematic behavior, largely impairing individual''s health, wealth and well‐being. Substantial studies have confirmed that conscientiousness, one of the big five personality, showed markedly inverse relation with procrastination. However, it is hitherto unknown about the neural basis underlying the impact of conscientiousness on procrastination. To address this issue, we employed the voxel‐based morphometry (VBM) and resting‐state functional connectivity (RSFC) methods to explore the neural substrates of conscientiousness responsible for procrastination (N = 330). In line with previous findings, the behavioral results showed a strong negative correlation between conscientiousness and procrastination (r = −.75). The VBM analysis found that conscientiousness was positively correlated with gray matter (GM) volumes in the left dorsal‐lateral prefrontal cortex (dlPFC), right orbital frontal cortex (OFC) and right putamen, but negatively correlated with that in the left insula. Moreover, the RSFC results revealed that both dlPFC‐IPL (inferior parietal lobule) and dlPFC‐PCC (posterior cingulate gyrus) functional connectivity were positively associated with conscientiousness, while the functional connectivity of parahippocampal gyrus (PHC)‐putamen and insula‐IPL were negatively associated with conscientiousness. More importantly, the structural equation modeling (SEM) integrating RSFC results were well fitted for the influence process of conscientiousness on procrastination by both self‐control (i.e., dlPFC‐IPL, dlPFC‐PCC) and motivation pathways (i.e., PHC‐putamen, insula‐IPL). The current findings suggest that self‐control and motivation could be the two neural pathways underlying the impact of conscientiousness on procrastination, which provides a new perspective to understand the relationship between conscientiousness and procrastination.     

Neural oscillatory responses to performance monitoring differ between high‐ and low‐impulsive individuals,but are unaffected by TMS

Higher impulsivity may arise from neurophysiological deficits of cognitive control in the prefrontal cortex. Cognitive control can be assessed by time‐frequency decompositions of electrophysiological data. We aimed to clarify neuroelectric mechanisms of performance monitoring in connection with impulsiveness during a modified Eriksen flanker task in high‐ (n = 24) and low‐impulsive subjects (n = 21) and whether these are modulated by double‐blind, sham‐controlled intermittent theta burst stimulation (iTBS). We found a larger error‐specific peri‐response beta power decrease over fronto‐central sites in high‐impulsive compared to low‐impulsive participants, presumably indexing less effective motor execution processes. Lower parieto‐occipital theta intertrial phase coherence (ITPC) preceding correct responses predicted higher reaction time (RT) and higher RT variability, potentially reflecting efficacy of cognitive control or general attention. Single‐trial preresponse theta phase clustering was coupled to RT in correct trials (weighted ITPC), reflecting oscillatory dynamics that predict trial‐specific behavior. iTBS did not modulate behavior or EEG time‐frequency power. Performance monitoring was associated with time‐frequency patterns reflecting cognitive control (parieto‐occipital theta ITPC, theta weighted ITPC) as well as differential action planning/execution processes linked to trait impulsivity (frontal low beta power). Beyond that, results suggest no stimulation effect related to response‐locked time‐frequency dynamics with the current stimulation protocol. Neural oscillatory responses to performance monitoring differ between high‐ and low‐impulsive individuals, but are unaffected by iTBS.     

Modulation of the rat hippocampal‐cortex network and episodic‐like memory performance following entorhinal cortex stimulation

AimsEntorhinal cortex (EC) deep brain stimulation (DBS) has shown a memory enhancement effect. However, its brain network modulation mechanisms remain unclear. The present study aimed to investigate the functional connectivity in the rat hippocampal‐cortex network and episodic‐like memory performance following EC‐DBS.Methods7.0 T functional MRI (fMRI) scans and episodic‐like memory tests were performed 3 days and 28 days after EC‐DBS in healthy rats. The fMRI data processing was focused on the power spectra, functional connectivity, and causality relationships in the hippocampal‐cortex network. In addition, the exploration ratio for each object and the discrimination ratio of the “when” and “where” factors were calculated in the behavioral tests.ResultsEC‐DBS increased the power spectra and the functional connectivity in the prefrontal‐ and hippocampal‐related networks 3 days after stimulation and recovered 4 weeks later. Both networks exhibited a strengthened connection with the EC after EC‐DBS. Further seed‐based functional connectivity comparisons showed increased connectivity among the prefrontal cortex, hippocampus and EC, especially on the ipsilateral side of DBS. The dentate gyrus is a hub region closely related to both the EC and the prefrontal cortex and receives information flow from both. Moreover, acute EC‐DBS also enhanced the discrimination ratio of the “where” factor in the episodic‐like memory test on Day 3.ConclusionEC‐DBS caused a reversible modulation effect on functional connectivity in the hippocampal‐cortex network and episodic‐like memory performance.     

Stimulus transformation into motor action: Dynamic graph analysis reveals a posterior‐to‐anterior shift in brain network communication of older subjects

Cognitive performance slows down with increasing age. This includes cognitive processes that are essential for the performance of a motor act, such as the slowing down in response to an external stimulus. The objective of this study was to identify aging‐associated functional changes in the brain networks that are involved in the transformation of external stimuli into motor action. To investigate this topic, we employed dynamic graphs based on phase‐locking of Electroencephalography signals recorded from healthy younger and older subjects while performing a simple visually‐cued finger‐tapping task. The network analysis yielded specific age‐related network structures varying in time in the low frequencies (2–7 Hz), which are closely connected to stimulus processing, movement initiation and execution in both age groups. The networks in older subjects, however, contained several additional, particularly interhemispheric, connections and showed an overall increased coupling density. Cluster analyses revealed reduced variability of the subnetworks in older subjects, particularly during movement preparation. In younger subjects, occipital, parietal, sensorimotor and central regions were—temporally arranged in this order—heavily involved in hub nodes. Whereas in older subjects, a hub in frontal regions preceded the noticeably delayed occurrence of sensorimotor hubs, indicating different neural information processing in older subjects. All observed changes in brain network organization, which are based on neural synchronization in the low frequencies, provide a possible neural mechanism underlying previous fMRI data, which report an overactivation, especially in the prefrontal and pre‐motor areas, associated with a loss of hemispheric lateralization in older subjects.     

Effective connectivity between bed nucleus of the stria terminalis and amygdala: Reproducibility and relation to anxiety

In a previous study, we investigated the resting‐state fMRI effective connectivity (EC) between the bed nucleus of the stria terminalis (BNST) and the laterobasal (LB), centromedial (CM), and superficial (SF) amygdala. We found strong negative EC from all amygdala nuclei to the BNST, while the BNST showed positive EC to the amygdala. However, the validity of these findings remains unclear, since a reproduction in different samples has not been done. Moreover, the association of EC with measures of anxiety offers deeper insight, due to the known role of the BNST and amygdala in fear and anxiety. Here, we aimed to reproduce our previous results in three additional samples. We used spectral Dynamic Causal Modeling to estimate the EC between the BNST, the LB, CM, and SF, and its association with two measures of self‐reported anxiety. Our results revealed consistency over samples with regard to the negative EC from the amygdala nuclei to the BNST, while the positive EC from BNST to the amygdala was also found, but weaker and more heterogenic. Moreover, we found the BNST‐BNST EC showing a positive and the CM‐BNST EC, showing a negative association with anxiety. Our study suggests a reproducible pattern of negative EC from the amygdala to the BNST along with weaker positive EC from the BNST to the amygdala. Moreover, less BNST self‐inhibition and more inhibitory influence from the CM to the BNST seems to be a pattern of EC that is related to higher anxiety.     

Aberrant dynamic structure–function relationship of rich‐club organization in treatment‐naïve newly diagnosed juvenile myoclonic epilepsy

Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich‐club organization—a core feature of the brain networks. Moreover, it is unclear how structure–function relationships dynamically change over time in JME. Here, we quantify the anatomical rich‐club organization and dynamic structural and functional connectivity (SC–FC) coupling in 47 treatment‐naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC–FC coupling were also calculated to examine the supporting of structure–function relationship to brain information transfer. The results showed that the anatomical rich‐club organization was disrupted in the patient group, along with decreased connectivity strength among rich‐club hub nodes. Furthermore, reduced SC–FC coupling in rich‐club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor‐cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC–FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC–FC coupling of rich‐club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.     

Mind the gap: Performance metric evaluation in brain‐age prediction

Estimating age based on neuroimaging‐derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine‐learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population‐based datasets, and assessed the effects of age range, sample size and age‐bias correction on the model performance metrics Pearson''s correlation coefficient (r), the coefficient of determination (R ²), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R ² values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age‐bias corrected metrics indicate high accuracy—also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study‐specific data characteristics, and cannot be directly compared across different studies. Since age‐bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.     

Targeting brain regions of interest in functional near‐infrared spectroscopy—Scalp‐cortex correlation using subject‐specific light propagation models

Targeting specific brain regions of interest by the accurate positioning of optodes (emission and detection probes) on the scalp remains a challenge for functional near‐infrared spectroscopy (fNIRS). Since fNIRS data does not provide any anatomical information on the brain cortex, establishing the scalp‐cortex correlation (SCC) between emission‐detection probe pairs on the scalp and the underlying brain regions in fNIRS measurements is extremely important. A conventional SCC is obtained by a geometrical point‐to‐point manner and ignores the effect of light scattering in the head tissue that occurs in actual fNIRS measurements. Here, we developed a sensitivity‐based matching (SBM) method that incorporated the broad spatial sensitivity of the probe pair due to light scattering into the SCC for fNIRS. The SCC was analyzed between head surface fiducial points determined by the international 10–10 system and automated anatomical labeling brain regions for 45 subject‐specific head models. The performance of the SBM method was compared with that of three conventional geometrical matching (GM) methods. We reveal that the light scattering and individual anatomical differences in the head affect the SCC, which indicates that the SBM method is compulsory to obtain the precise SCC. The SBM method enables us to evaluate the activity of cortical regions that are overlooked in the SCC obtained by conventional GM methods. Together, the SBM method could be a promising approach to guide fNIRS users in designing their probe arrangements and in explaining their measurement data.     

Reliability of task‐evoked neural activation during face‐emotion paradigms: Effects of scanner and psychological processes

Assessing and improving test–retest reliability is critical to efforts to address concerns about replicability of task‐based functional magnetic resonance imaging. The current study uses two statistical approaches to examine how scanner and task‐related factors influence reliability of neural response to face‐emotion viewing. Forty healthy adult participants completed two face‐emotion paradigms at up to three scanning sessions across two scanners of the same build over approximately 2 months. We examined reliability across the main task contrasts using Bayesian linear mixed‐effects models performed voxel‐wise across the brain. We also used a novel Bayesian hierarchical model across a predefined whole‐brain parcellation scheme and subcortical anatomical regions. Scanner differences accounted for minimal variance in temporal signal‐to‐noise ratio and task contrast maps. Regions activated during task at the group level showed higher reliability relative to regions not activated significantly at the group level. Greater reliability was found for contrasts involving conditions with clearly distinct visual stimuli and associated cognitive demands (e.g., face vs. nonface discrimination) compared to conditions with more similar demands (e.g., angry vs. happy face discrimination). Voxel‐wise reliability estimates tended to be higher than those based on predefined anatomical regions. This work informs attempts to improve reliability in the context of task activation patterns and specific task contrasts. Our study provides a new method to estimate reliability across a large number of regions of interest and can inform researchers'' selection of task conditions and analytic contrasts.     

Altered brain functional network dynamics in obsessive–compulsive disorder

Obsessive–compulsive disorder (OCD) is a debilitating and disabling neuropsychiatric disorder, whose neurobiological basis remains unclear. Although traditional static resting‐state magnetic resonance imaging (rfMRI) studies have found aberrant functional connectivity (FC) in OCD, alterations in whole‐brain FC and topological properties in the context of brain dynamics remain relatively unexplored. The rfMRI data of 29 patients with OCD and 40 healthy controls were analyzed using group independent component analysis to obtain independent components (ICs) and a sliding‐window approach to generate dynamic functional connectivity (dFC) matrices. dFC patterns were clustered into three reoccurring states, and state transition metrics were obtained. Then, graph‐theory methods were applied to dFC matrices to calculate the variability of network topological organization. The occurrence of a state (State 1) with the highest modularity index and lowest mean FC between networks was increased significantly in OCD, and the fractional time in brain State 1 was positively correlated with anxiety level in patients. State 1 was characterized by having positive connections within default mode (DMN) and salience networks (SAN), and negative coupling between the two networks. Additionally, ICs belonging to DMN and SAN showed lower temporal variability of nodal degree centrality and efficiency in patients, which was related to longer illness duration and higher current obsession ratings. Our results provide evidence of clinically relevant aberrant dynamic brain activity in OCD. Increased functional segregation among networks and impaired functional flexibility in connections among brain regions in DMN and SAN may play important roles in the neuropathology of OCD.