A machine-learning approach to model risk and protective factors of vulnerability to depression.

There are multiple risk and protective factors for depression. The association between these factors with vulnerability to depression is unclear. Such knowledge is an important insight into assessing risk for developing depression for precision interventions. Based on the behavioral data of 496 participants (all unmarried and not cohabiting, with a college education level or above), we applied machine-learning approaches to model risk and protective factors in estimating depression and its symptoms. Then, we employed Random Forest to identify important factors which were then used to differentiate participants who had high risk of depression from those who had low risk. Results revealed that risk and protective factors could significantly estimate depression and depressive symptoms. Feature selection revealed four key factors including three risk factors (brooding, perceived loneliness, and perceived stress) and one protective factor (resilience). The classification model built by the four factors achieved an ROC-AUC score of 75.50% to classify the high- and low-risk groups, which was comparable to the classification performance based on all risk and protective factors (ROC-AUC = 77.83%). Based on the selected four factors, we generated a mood vulnerability index useful for identifying people's risk for depression. Our findings provide potential clinical insights for developing quick screening tools for mood disorders and potential targets for intervention programs designed to improve depressive symptoms.

Value of contrast-enhanced MR angiography for the follow-up of treated brain arteriovenous malformations: systematic review and meta-analysis.

OBJECTIVE: To estimate the diagnostic value of contrast-enhanced MR angiography (CE-MRA) in identifying residual brain arteriovenous malformations (AVMs) after treatment. METHODS: We retrieved appropriate references from the electronic databases of PubMed, Web of Science, Embase, and Cochrane Library, and then evaluated the methodology quality of included references using the QUADAS-2 tool. We calculated the pooled sensitivity and specificity by applying a bivariate mixed-effects model and detected the publication bias using Deeks' funnel plot. The values of I2 were used to test heterogeneity and meta-regression analyses were performed to search for the causes of heterogeneity. RESULTS: We included 7 eligible studies containing 223 participants. Compared with a gold standard, the overall sensitivity and specificity of CE-MRA in detecting residual brain AVMs were 0.77 (95% CI 0.65-0.86) and 0.97 (95% CI 0.82-1.00), respectively. Based on the summary ROC curve, the AUC was 0.89 (95% CI 0.86-0.92). Heterogeneity could be observed in our study, especially for the specificity (I2 = 74.23%). Furthermore, there was no evidence of publication bias. CONCLUSIONS: Our study provides evidence that CE-MRA has good diagnostic value and specificity for the follow-up of treated brain AVMs. Nevertheless, considering the small sample size, heterogeneity, and many factors that may affect the diagnostic accuracy, future large-sample, prospective studies are necessary to validate the results. KEY POINTS: • The pooled sensitivity and specificity of contrast-enhanced MR angiography (CE-MRA) in detecting residual arteriovenous malformations (AVMs) were 0.77 (95% CI 0.65-0.86) and 0.97 (95% CI 0.82-1.00). • The four-dimensional CE-MRA showed less sensitivity than the three-dimensional CE-MRA for treated AVMs. • CE-MRA is helpful to identify residual AVMs and reduce excessive DSA during follow-up.

Modeling Co-Infection by Streptococcus suis and Haemophilus parasuis Reveals Influences on Biofilm Formation and Host Response.

Streptococcus suis (S. suis) and Haemophilus parasuis (H. parasuis) are two primary pathogens currently affecting the porcine industry. They often cause encephalitis and arthritis. They also frequently co-infect in clinical settings. In the current study, we identified significant correlations between S. suis and H. parasuis. The results from CI versus RIR suggested that S. suis and H. parasuis were competitive in general. Compared to mono-species biofilm, the biomass, bio-volume, and thickness of mixed-species biofilms were significantly higher, which was confirmed using crystal violet staining, confocal laser scanning microscopy, and scanning electron microscopy. Compared to mono-species biofilm, the viable bacteria in the mixed-species biofilms were significantly lower, which was confirmed using the enumeration of colony-forming units (CFU cm-2). The susceptibility of antibiotics in the co-culture decreased in the planktonic state. In contrast, biofilm state bacteria are significantly more difficult to eradicate with antibiotics than in a planktonic state. Whether in planktonic or biofilm state, the expression of virulence genes of S. suis and H. parasuis in mixed culture was very different from that in single culture. Subsequently, by establishing a mixed infection model in mice, we found that the colonization of the two pathogens in organs increased after mixed infection, and altered the host's inflammatory response. In summary, our results indicate that S. suis and H. parasuis compete when co-cultured in vitro. Surprisingly, S. suis and H. parasuis synergistically increased colonization capacity after co-infection in vivo. This study elucidated the interaction between S. suis and H. parasuis during single infections and co-infections. Future studies on bacterial disease control and antibiotic treatment should consider the interaction of mixed species.

Safety and Efficacy of Edaravone in Patients with Amyotrophic Lateral Sclerosis: A Systematic Review and Meta-analysis.

BACKGROUND AND OBJECTIVE: The efficacy and safety of edaravone for the treatment of amyotrophic lateral sclerosis (ALS) remain unclear. The aim of this meta-analysis was to provide evidence-based medical guidance and advice for the clinical application of edaravone in the treatment of ALS. METHODS: PubMed, Embase, Chinese Biomedical Literature Database (CBM), Cochrane Library and Web of Science were searched through 09 March 2022 for randomized controlled trials (RCTs) on the safety and efficacy of edaravone versus placebo during follow-up of patients with ALS. A summary of the outcome measures with GRADE was performed. This study was registered on PROSPERO (ID: CRD 42022319997). RESULTS: Five RCTs with a total of 566 participants were included, and there was a significant difference (mean difference [MD] 1.33, 95% confidence interval [CI] 0.33-2.34; p = 0.009) in the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS-R) score between the treatment and placebo groups. The edaravone group had an increased grip strength (MD 0.26, 95% CI 0.03-0.49; p = 0.03) and modified Norris Scale score (MD 2.81, 95% CI 1.18-4.43; p = 0.0007). However, there were no significant differences between groups for the change in forced vital capacity (FVC)% (MD 0.55, 95% CI - 3.15 to 4.24; p = 0.77), pinch strength (MD 0.05, 95% CI - 0.05 to 0.16; p = 0.33) or Amyotrophic Lateral Sclerosis Assessment Questionnaire (ALSAQ-40) score (MD - 4.76, 95% CI - 9.56 to 0.03; p = 0.05). The incidence of adverse events (AEs) (risk ratio [RR] 0.09, 95% CI 0.93-1.05; p = 0.65), serious adverse events (SAEs) (RR 0.72, 95% CI 0.52-1.00; p = 0.05) and the number of deaths (risk difference [RD] 0.00, 95% CI - 0.02 to 0.03; p = 0.83) were not statistically different from the placebo group. The quality of evidence was low only for SAEs, and the remaining outcome measures were of moderate quality. CONCLUSIONS: Compared with placebo, edaravone may provide potential clinical benefits in the treatment of ALS and may not increase the number of AEs and deaths. However, due to the low-quality evidence of the included studies and the small sample size, more high-quality and high-standard research evidence is needed to confirm these results. PROTOCOL REGISTRATION: This study was registered on PROSPERO (ID: CRD 42022319997).

Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease.

Moyamoya disease is a rare cerebrovascular disorder associated with cognitive dysfunction. It is usually treated by surgical revascularization, but research on the neurocognitive outcomes of revascularization surgery is controversial. Given that neurocognitive impairment could affect the daily activities of patients with moyamoya disease, early detection of postoperative neurocognitive outcomes has the potential to improve patient management. In this study, we applied a well-established connectome-based predictive modelling approach to develop machine learning models that used preoperative resting-state functional connectivity to predict postoperative changes in processing speed in patients with moyamoya disease. Twelve adult patients with moyamoya disease (age range: 23-49 years; female/male: 9/3) were recruited prior to surgery and underwent follow-up at 1 and 6 months after surgery. Twenty healthy controls (age range: 24-54 years; female/male: 14/6) were recruited and completed the behavioural test at baseline, 1-month follow-up and 6-month follow-up. Behavioural results indicated that the behavioural changes in processing speed at 1 and 6 months after surgery compared with baseline were not significant. Importantly, we showed that preoperative resting-state functional connectivity significantly predicted postoperative changes in processing speed at 1 month after surgery (negative network: ρ = 0.63, P corr = 0.017) and 6 months after surgery (positive network: ρ = 0.62, P corr = 0.010; negative network: ρ = 0.55, P corr = 0.010). We also identified cerebro-cerebellar and cortico-subcortical connectivities that were consistently associated with processing speed. The brain regions identified from our predictive models are not only consistent with previous studies but also extend previous findings by revealing their potential roles in postoperative neurocognitive functions in patients with moyamoya disease. Taken together, our findings provide preliminary evidence that preoperative resting-state functional connectivity might predict the post-surgical longitudinal neurocognitive changes in patients with moyamoya disease. Given that processing speed is a crucial cognitive ability supporting higher neurocognitive functions, this study's findings offer important insight into the clinical management of patients with moyamoya disease.

Specific quantitative detection of Streptococcus suis and Actinobacillus pleuropneumoniae in co-infection and mixed biofilms.

Respiratory infections seriously affect the swine industry worldwide. Co-infections of two vital pathogenic bacteria Streptococcus suis (S. suis) and Actinobacillus pleuropneumoniae (A. pleuropneumoniae), colonizing the respiratory tract often occurs in veterinary clinical practice. Moreover, our previous research found that S. suis and A. pleuropneumoniae can form biofilm in vitro. The formation of a mixed biofilm not only causes persistent infections, but also increases the multiple drug resistance of bacteria, which brings difficulties to disease prevention and control. However, the methods for detecting S. suis and A. pleuropneumoniae in co-infection and biofilm are immature. Therefore, in this study, primers and probes were designed based on the conservative sequence of S. suis gdh gene and A. pleuropneumoniae apxIVA gene. Then, a TaqMan duplex real-time PCR method for simultaneous detection of S. suis and A. pleuropneumoniae was successfully established via optimizing the reaction system and conditions. The specificity analysis results showed that this TaqMan real-time PCR method had strong specificity and high reliability. The sensitivity test results showed that the minimum detection concentration of S. suis and A. pleuropneumoniae recombinant plasmid was 10 copies/µL, which is 100 times more sensitive than conventional PCR methods. The amplification efficiencies of S. suis and A. pleuropneumoniae were 95.9% and 104.4% with R2 value greater than 0.995, respectively. The slopes of the calibration curves of absolute cell abundance of S. suis and A. pleuropneumoniae were 1.02 and 1.09, respectively. The assays were applied to cultivated mixed biofilms and approximately 108 CFUs per biofilm were quantified when 108 CFUs planktonic bacteria of either S. suis or A. pleuropneumoniae were added to biofilms. In summary, this study developed a TaqMan real-time PCR assay for specific, accurate quantification of S. suis or A. pleuropneumoniae in mixed biofilms, which may help for the detection, prevention and control of diseases caused by a bacterial mixed infection involving S. suis and A. pleuropneumoniae.

Multimodal Neural Evidence on the Corticostriatal Underpinning of Suicidality in Late-Life Depression.

BACKGROUND: Suicidality involves thoughts (ideations and plans) and actions related to self-inflicted death. To improve management and prevention of suicidality, it is essential to understand the key neural mechanisms underlying suicidal thoughts and actions. Following empirically informed neural framework, we hypothesized that suicidal thoughts would be primarily characterized by alterations in the default mode network indicating disrupted self-related processing, whereas suicidal actions would be characterized by changes in the lateral prefrontal corticostriatal circuitries implicating compromised action control. METHODS: We analyzed the gray matter volume and resting-state functional connectivity of 113 individuals with late-life depression, including 45 nonsuicidal patients, 33 with suicidal thoughts but no action, and 35 with past suicidal action. Between-group analyses revealed key neural features associated with suicidality. The functional directionality of the identified resting-state functional connectivity was examined using dynamic causal modeling to further elucidate its mechanistic nature. Post hoc classification analysis examined the contribution of the neural measures to suicide classification. RESULTS: As expected, reduced gray matter volumes in the default mode network and lateral prefrontal regions characterized patients with suicidal thoughts and those with past suicidal actions compared with nonsuicidal patients. Furthermore, region-of-interest analyses revealed that the directionality and strength of the ventrolateral prefrontal cortex-caudate resting-state functional connectivity were related to suicidal thoughts and actions. The neural features significantly improved classification of suicidal thoughts and actions over that based on clinical and suicide questionnaire variables. CONCLUSIONS: Gray matter reductions in the default mode network and lateral prefrontal regions and the ventrolateral prefrontal cortex-caudate connectivity alterations characterized suicidal thoughts and actions in patients with late-life depression.

Connectome-based models can predict processing speed in older adults.

Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process.

The relationship between loneliness and working-memory-related frontoparietal network connectivity in people with major depressive disorder.

Loneliness affects up to 40 % of middle-aged and older adults, and is closely associated with major depressive disorder (MDD). However, the relationship between loneliness and neural network functioning during executive cognitive processes, such as working memory, in MDD is still unclear. To address this gap, our study recruited 21 medicated MDD patients (mean age = 52.0 ± 5 years) and 24 matched healthy controls (HC) (mean age = 48.7 ± 6 years) who completed an n-back fMRI task. For behavioural performance, we observed no significant moderating effect of MDD or loneliness on the task condition effect. However, loneliness was positively associated, and MDD was negatively associated, with the functional connectivity between the inferior parietal cortex and the rostral dorsomedial prefrontal cortex (DMPFC) during task performance. Furthermore, an interactive effect of loneliness and MDD was observed on the functional connectivity between the supplementary motor area and the caudal DMPFC during the n-back task, with loneliness showing a positive relationship in the HC group but a negative relationship in the MDD group with the connectivity. Our results indicated that loneliness may be associated with altered neural regulatory functioning on self-referential processing and action control, which may further depend on the individual's depressive state. These findings can form the theoretical basis for devising intervention programme aimed at improving the mental wellness of the healthy and depressed lonely individuals.

Effective Self-Management for Early Career Researchers in the Natural and Life Sciences.

Early career researchers (ECRs) are faced with a range of competing pressures in academia, making self-management key to building a successful career. The Organization for Human Brain Mapping undertook a group effort to gather helpful advice for ECRs in self-management.

Loneliness and depression dissociated on parietal-centered networks in cognitive and resting states.

BACKGROUND: Perceived loneliness, an increasingly prevalent social issue, is closely associated with major depressive disorder (MDD). However, the neural mechanisms previously implicated in key cognitive and affective processes in loneliness and MDD still remain unclear. Such understanding is critical for delineating the psychobiological basis of the relationship between loneliness and MDD. METHODS: We isolated the unique and interactive cognitive and neural substrates of loneliness and MDD among 27 MDD patients (mean age = 51.85 years, 20 females), and 25 matched healthy controls (HCs; mean age = 48.72 years, 19 females). We assessed participants' behavioral performance and neural regional and network functions on a Stroop color-word task, and their resting-state neural connectivity. RESULTS: Behaviorally, we found greater incongruence-related accuracy cost in MDD patients, but reduced incongruence effect on reaction time in lonelier individuals. When performing the Stroop task, loneliness positively predicted prefrontal-anterior cingulate-parietal connectivity across all participants, whereas MDD patients showed a decrease in connectivity compared to controls. Furthermore, loneliness negatively predicted parietal and cerebellar activities in MDD patients, but positively predicted the same activities in HCs. During resting state, MDD patients showed reduced parietal-anterior cingulate connectivity, which again positively correlated with loneliness in this group. CONCLUSIONS: We speculate the distinct neurocognitive profile of loneliness might indicate increase in both bottom-up attention and top-down executive control functions. However, the upregulated cognitive control processes in lonely individuals may eventually become exhausted, which may in turn predispose to MDD onset.

Eyes Closed Elevates Brain Intrinsic Activity of Sensory Dominance Networks: A Classifier Discrimination Analysis.

Brain neocortex is usually dominated by visual input (with eyes open [EO]), whereas this visual predominance could be reduced by closing eyes. Cutting off visual input from the eyes (with eyes closed [EC]) would also benefit other sensory performance; however, the neural basis underlying the state-switching remains unclear. In this study, we investigated the brain intrinsic activity of either the EO or EC states by using the resting-state functional magnetic resonance imaging data from 22 healthy participants. The 10 resting-state networks (RSNs) of these participants were explored by the independent component analysis method. Within each RSN, various network parameters (i.e., the amplitude of low-frequency fluctuation, the voxel-wise weighted degree centrality, and the RSN-wise functional connectivity) were measured to depict the brain intrinsic activity properties underlying the EO and EC states. Taking these brain intrinsic activity properties as discriminative features in a linear classifier, we found that the EO and EC states could be effectively classified using the intrinsic properties of the sensory dominance networks and the salience network (SN). Further analysis showed that the brain intrinsic activity within the sensory dominance networks was constantly overwhelmed during the EC state relative to that in the EO state. The SN might play a key role as a switcher between state-switching. Therefore, this study indicated that the brain intrinsic activity in the sensory dominance networks would be enhanced with EC, which might improve other sensory-relative task performance.

Relation of visual creative imagery manipulation to resting-state brain oscillations.

Visual creative imagery (VCI) manipulation is the key component of visual creativity; however, it remains largely unclear how it occurs in the brain. The present study investigated the brain neural response to VCI manipulation and its relation to intrinsic brain activity. We collected functional magnetic resonance imaging (fMRI) datasets related to a VCI task and a control task as well as pre- and post-task resting states in sequential sessions. A general linear model (GLM) was subsequently used to assess the specific activation of the VCI task compared with the control task. The changes in brain oscillation amplitudes across the pre-, on-, and post-task states were measured to investigate the modulation of the VCI task. Furthermore, we applied a Granger causal analysis (GCA) to demonstrate the dynamic neural interactions that underlie the modulation effect. We determined that the VCI task specifically activated the left inferior frontal gyrus pars triangularis (IFGtriang) and the right superior frontal gyrus (SFG), as well as the temporoparietal areas, including the left inferior temporal gyrus, right precuneus, and bilateral superior parietal gyrus. Furthermore, the VCI task modulated the intrinsic brain activity of the right IFGtriang (0.01-0.08 Hz) and the left caudate nucleus (0.2-0.25 Hz). Importantly, an inhibitory effect (negative) may exist from the left SFG to the right IFGtriang in the on-VCI task state, in the frequency of 0.01-0.08 Hz, whereas this effect shifted to an excitatory effect (positive) in the subsequent post-task resting state. Taken together, the present findings provide experimental evidence for the existence of a common mechanism that governs the brain activity of many regions at resting state and whose neural activity may engage during the VCI manipulation task, which may facilitate an understanding of the neural substrate of visual creativity.

Association between resting-state brain network topological organization and creative ability: Evidence from a multiple linear regression model.

Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability.

Exploring the Associations Between Intrinsic Brain Connectivity and Creative Ability Using Functional Connectivity Strength and Connectome Analysis.

The present study aimed to explore the association between resting-state functional connectivity and creativity ability. Toward this end, the figural Torrance Tests of Creative Thinking (TTCT) scores were collected from 180 participants. Based on the figural TTCT measures, we collected resting-state functional magnetic resonance imaging data for participants with two different levels of creativity ability (a high-creativity group [HG, n = 22] and a low-creativity group [LG, n = 20]). For the aspect of group difference, this study combined voxel-wise functional connectivity strength (FCS) and seed-based functional connectivity to identify brain regions with group-change functional connectivity. Furthermore, the connectome properties of the identified regions and their associations with creativity were investigated using the permutation test, discriminative analysis, and brain-behavior correlation analysis. The results indicated that there were 4 regions with group differences in FCS, and these regions were linked to 30 other regions, demonstrating different functional connectivity between the groups. Together, these regions form a creativity-related network, and we observed higher network efficiency in the HG compared with the LG. The regions involved in the creativity network were widely distributed across the modality-specific/supramodality cerebral cortex, subcortex, and cerebellum. Notably, properties of regions in the supramodality networks (i.e., the default mode network and attention network) carried creativity-level discriminative information and were significantly correlated with the creativity performance. Together, these findings demonstrate a link between intrinsic brain connectivity and creative ability, which should provide new insights into the neural basis of creativity.

High transition frequencies of dynamic functional connectivity states in the creative brain.

Creativity is thought to require the flexible reconfiguration of multiple brain regions that interact in transient and complex communication patterns. In contrast to prior emphases on searching for specific regions or networks associated with creative performance, we focused on exploring the association between the reconfiguration of dynamic functional connectivity states and creative ability. We hypothesized that a high frequency of dynamic functional connectivity state transitions will be associated with creative ability. To test this hypothesis, we recruited a high-creative group (HCG) and a low-creative group (LCG) of participants and collected resting-state fMRI (R-fMRI) data and Torrance Tests of Creative Thinking (TTCT) scores from each participant. By combining an independent component analysis with a dynamic network analysis approach, we discovered the HCG had more frequent transitions between dynamic functional connectivity (dFC) states than the LCG. Moreover, a confirmatory analysis using multiplication of temporal derivatives also indicated that there were more frequent dFC state transitions in the HCG. Taken together, these results provided empirical evidence for a linkage between the flexible reconfiguration of dynamic functional connectivity states and creative ability. These findings have the potential to provide new insights into the neural basis of creativity.

Mental rotation task specifically modulates functional connectivity strength of intrinsic brain activity in low frequency domains: A maximum uncertainty linear discriminant analysis.

Neuroimaging studies have highlighted that intrinsic brain activity is modified to implement task demands. However, the relation between mental rotation and intrinsic brain activity remains unclear. To answer this question, we collected functional MRI (fMRI) data from 30 healthy participants in two mental rotation task periods (1st-task state, 2nd-task state) and two rest periods before (pre-task resting state) and after the task (post-task resting state) respectively. By combining the spatial independent component analysis (ICA) and voxel-wise functional connectivity strength (FCS), we identified FCS maps of 10 brain resting state networks (RSNs) within six different bands (i.e., 0-0.05, 0.05-0.1, 0.1-0.15, 0.15-0.2, 0.2-0.25, and 0.01-0.08Hz) corresponding to the four states for each subject. The maximum uncertainty linear discriminant analysis (MLDA) method showed that the FCS within the low frequency bandwidth of 0.05-0.1Hz could effectively classify the mental rotation task state from pre-/post-task resting states but failed to discriminate the pre- and post-task resting states. Discriminative FCSs were observed in the cognitive executive-control network (central executive and attention) and the imagery-based internal mental manipulation network (default mode, primary sensorimotor, and primary visual). Imagery manipulation is a stable mental element of mental rotation, and the involvement of executive control is dependent on the degree of task familiarity. Together, the present study provides evidence that mental rotation task specifically modifies intrinsic brain activity to complement cognitive demands, which provides further insight into the neural basis of mental rotation manipulation.