Resting State Network Activity Research Articles

Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis.

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called Leading Eigenvector Dynamics Analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, inter group differences in brain dynamic activity indicators are examined. And the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual regions activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state, and more active in limbic regions activity state and visual regions activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.

Rorschach and resting state-fMRI: Personality Assessment and unique associations with resting-state brain networks activity

Background The Rorschach test is one of the most popular tests used in clinical settings for psychopathology and personality assessment; however, there is still little evidence regarding the functional correlates of test responses. Functional magnetic resonance imaging (fMRI) allows for the investigation of biological correlates associated with different psychological functions. Among other applications, fMRI has been used to identify different networks that reflect brain activity in the resting state (rs-fMRI), that is, when an individual is not engaged in any specific task. Among these resting-state networks (RSN), the best-known and most studied are the Default Mode Network (DMN), along with other networks such as salience, frontoparietal, sensorimotor, temporo-parietal, visual, and cerebellar networks. Methods We used this type of analysis to obtain new evidence regarding Rorschach. This study aimed to analyze the functional brain architecture underlying Rorschach’s personality variables, administered according to Exner’s Comprehensive System (CS). In particular, the aims are: 1) to ascertain the existence of correlations between CS variables and activity of the RSN, and 2) to use these profiles of activity to develop a new data-driven clustering of the CS variables. Archival data from twenty-four non-clinical subjects were analyzed. Independent Component Analysis (ICA) and partial least squares regression (PLS) were used to analyze the fMRI data. Results The results showed specific associations with the given Rorschach variables (several of which could be grouped into higher-order latent factors) and activity of the main RSN. Moreover, the cluster analysis outlined important groupings of Rorschach variables, particularly regarding their clinical implications. Conclusions Our study could be comprised in the existing literature providing strong evidence about the neurobiological validity of the Rorschach test.

Neurofeedback for alcohol addiction: Changes in resting state network activity✰

Alcohol dependence continues to be a major global burden despite significant research progress and treatment development. The aim of this study was to investigate whether neurofeedback training can alter resting state fMRI activity in brain regions that play a crucial role in addiction disorders in patients with alcohol dependence. For this purpose, a total of 52 patients were recruited for the present study, randomized, and divided into an active and a sham group. Patients in the active group received three sessions of neurofeedback training. We compared the resting state data in the active group as part of the NF training on six measurement days. When comparing the results of the active group from neurofeedback day 3 with baseline 1, a significant reduction in activated voxels in the ventral attention network area was seen. This suggests that reduced activity over the course of therapy in subjects may lead to greater independence from external stimuli. Overall, a global decrease in activated voxels within all three analysed networks compared to baseline was observed in the study. The use of resting-state data as potential biomarkers, as activity changes within these networks, may be to help restore cognitive processes and alcohol abuse-related craving and emotions.

The effects of cannabis abstinence on cognition and resting state network activity in people with multiple sclerosis: A preliminary study

We previously reported that people with multiple sclerosis (pwMS) who have been using cannabis frequently over many years can have significant cognitive improvements accompanied by concomitant task-specific changes in brain activation following 28 days of cannabis abstinence. We now hypothesize that the default Mode Network (DMN), known to modulate cognition, would also show an improved pattern of activation align with cognitive improvement following 28 days of drug abstinence. Thirty three cognitively impaired pwMS who were frequent cannabis users underwent a neuropsychological assessment and fMRI at baseline. Individuals were then assigned to a cannabis continuation (CC, n = 15) or withdrawal (CW, n = 18) group and the cognitive and imaging assessments were repeated after 28 days. Compliance with cannabis withdrawal was checked with regular urine monitoring. Following acquisition of resting state fMRI (rs-fMRI), data were processed using independent component analysis (ICA) to identify the DMN spatial map. Between and within group analyses were carried out using dual regression for voxel-wise comparisons of the DMN. Clusters of voxels were considered statistically significant if they survived threshold-free cluster enhancement (TFCE) correction at p < 0.05. The two groups were well matched demographically and neurologically at baseline. The dual regression analysis revealed no between group differences at baseline in the DMN. By day 28, the CW group in comparison to the CC group had increased activation in the left posterior cingulate, and right, angular gyrus (p < 0.05 for both, TFCE). A within group analysis for the CC group revealed no changes in resting state (RS) networks. Within group analysis of the CW group revealed increased activation at day 28 versus baseline in the left posterior cingulate, right angular gyrus, left hippocampus (BA 36), and the right medial prefrontal cortex (p < 0.05). The CW group showed significant improvements in multiple cognitive domains. In summary, our study revealed that abstaining from cannabis for 28 days reverses activation of DMN activity in pwMS in association with improved cognition across several domains.

Modulation of limbic resting-state networks by subthalamic nucleus deep brain stimulation.

Beyond the established effects of subthalamic nucleus deep brain stimulation (STN-DBS) in reducing motor symptoms in Parkinson's disease, recent evidence has highlighted the effect on non-motor symptoms. However, the impact of STN-DBS on disseminated networks remains unclear. This study aimed to perform a quantitative evaluation of network-specific modulation induced by STN-DBS using Leading Eigenvector Dynamics Analysis (LEiDA). We calculated the occupancy of resting-state networks (RSNs) in functional MRI data from 10 patients with Parkinson's disease implanted with STN-DBS and statistically compared between ON and OFF conditions. STN-DBS was found to specifically modulate the occupancy of networks overlapping with limbic RSNs. STN-DBS significantly increased the occupancy of an orbitofrontal limbic subsystem with respect to both DBS OFF (p = 0.0057) and 49 age-matched healthy controls (p = 0.0033). Occupancy of a diffuse limbic RSN was increased with STN-DBS OFF when compared with healthy controls (p = 0.021), but not when STN-DBS was ON, which indicates rebalancing of this network. These results highlight the modulatory effect of STN-DBS on components of the limbic system, particularly within the orbitofrontal cortex, a structure associated with reward processing. These results reinforce the value of quantitative biomarkers of RSN activity in evaluating the disseminated impact of brain stimulation techniques and the personalization of therapeutic strategies.

Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study.

Introduction: Resting-state network (RSN) connectivity is a widely used measure of the brain's functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand 11C-UCB-J PET. Methods: Independent component analyses (ICA) were performed on resting-state fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify a priori RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of 11C-UCB-J variability (medial prefrontal, striatal, and medial parietal). Pairwise correlations were performed to examine potential intermodal associations between the fractional amplitude of low-frequency fluctuations (fALFF) of RSNs and subject loadings of 11C-UCB-J source networks both locally and along known anatomical and functional pathways. Results: Greater medial prefrontal synaptic density was associated with greater fALFF of the anterior default-mode, posterior default-mode, and executive-control networks. Greater striatal synaptic density was associated with greater fALFF of the anterior default-mode and salience networks. Post-hoc mediation analyses exploring relationships between aging, synaptic density, and RSN activity revealed a significant indirect effect of greater age on fALFF of the anterior default-mode network mediated by the medial prefrontal 11C-UCB-J source. Discussion: RSN functional connectivity may be linked to synaptic architecture through multiple local and circuit-based associations. Findings regarding healthy aging, lower prefrontal synaptic density, and lower default-mode activity provide initial evidence of a neurophysiological link between RSN activity and local synaptic density, which may have relevance in neurodegenerative and psychiatric disorders.

Resting-state network organisation in children with traumatic brain injury

Children with traumatic brain injury are at risk of neurocognitive and behavioural impairment. Although there is evidence for abnormal brain activity in resting-state networks after TBI, the role of resting-state network organisation in paediatric TBI outcome remains poorly understood. This study is the first to investigate the impact of paediatric TBI on resting-state network organisation using graph theory, and its relevance for functional outcome.Participants were 8–14 years and included children with (i) mild TBI and risk factors for complicated TBI (mildRF+, n = 20), (ii) moderate/severe TBI (n = 15), and (iii) trauma control injuries (n = 27). Children underwent resting-state functional magnetic resonance imaging (fMRI), neurocognitive testing, and behavioural assessment at 2.8 years post-injury. Graph theory was applied to fMRI timeseries to evaluate the impact of TBI on global and local organisation of the resting-state network, and relevance for neurocognitive and behavioural functioning.Children with TBI showed atypical global network organisation as compared to the trauma control group, reflected by lower modularity (mildRF + TBI and moderate/severe TBI), higher smallworldness (mildRF + TBI) and lower assortativity (moderate/severe TBI ps < .04, Cohen's ds: > .6). Regarding local network organisation, the relative importance of hub regions in the network did not differ between groups. Regression analyses showed relationships between global as well as local network parameters with neurocognitive functioning (i.e., working memory, memory encoding; R2 = 23.3 - 38.5%) and behavioural functioning (i.e., externalising problems, R2 = 36.1%).Findings indicate the impact of TBI on global functional network organisation, and the relevance of both global and local network organisation for long-term neurocognitive and behavioural outcome after paediatric TBI. The results suggest potential prognostic value of resting-state network organisation for outcome after paediatric TBI.

Effects of modafinil on electroencephalographic microstates in healthy adults.

Modafinil has been proposed as a potentially effective clinical treatment for neuropsychiatric disorders characterized by cognitive control deficits. However, the precise effects of modafinil, particularly on brain network functions, are not completely understood. To address this gap, we examined the effects of modafinil on resting-state brain activity in 30 healthy adults using microstate analysis. Electroencephalographic (EEG) microstates are discrete voltage topographies generated from resting-state network activity. Using a placebo-controlled, within-subjects design, we examined changes to microstate parameters following placebo (0mg), low (100mg), and high (200mg) modafinil doses. We also examined the functional significance of these microstates via associations between microstate parameters and event-related potential indexes of conflict monitoring and automatic error processing (N2 and error-related negativity) and behavioral responses (accuracy and RT) from a subsequent flanker interference task. Five microstates emerged following each treatment condition, including four canonical microstates (A-D). Modafinil increased microstate C proportion and occurrence regardless of dose, relative to placebo. Modafinil also decreased microstate A proportion and microstate B proportion and occurrence relative to placebo. These modafinil-related changes in microstate parameters were not associated with similar changes in flanker ERPs or behavior. Finally, modafinil made transitions between microstates A and B less likely and transitions from A and B to C more likely. Previous fMRI work has correlated microstates A and B with auditory and visual networks and microstate C with a salience network. Thus, our results suggest modafinil may deactivate large-scale sensory networks in favor of a higher order functional network during resting-state in healthy adults.

Resting state networks mediate the association between both cardiovascular fitness and gross motor skills with neurocognitive functioning

Recent evidence suggests that cardiovascular fitness and gross motor skill performance are related to neurocognitive functioning by influencing brain structure and functioning. This study investigates the role of resting‐state networks (RSNs) in the relation of cardiovascular fitness and gross motor skills with neurocognitive functioning in healthy 8‐ to 11‐year‐old children (n = 90, 45 girls, 10% migration background). Cardiovascular fitness and gross motor skills were related to brain activity in RSNs. Furthermore, brain activity in RSNs mediated the relation of both cardiovascular fitness (Frontoparietal network and Somatomotor network) and gross motor skills (Somatomotor network) with neurocognitive functioning. The results indicate that brain functioning may contribute to the relation between both cardiovascular fitness and gross motor skills with neurocognitive functioning.

Effects of aerobic versus cognitively demanding exercise interventions on brain structure and function in healthy children-Results from a cluster randomized controlled trial.

The beneficial effects of physical activity on neurocognitive functioning in children are considered to be facilitated by physical activity‐induced changes in brain structure and functioning. In this study, we examined the effects of two 14‐week school‐based exercise interventions in healthy children on white matter microstructure and brain activity in resting‐state networks (RSNs) and whether changes in white matter microstructure and RSN activity mediate the effects of the exercise interventions on neurocognitive functioning. A total of 93 children were included in this study (51% girls, mean age 9.13 years). The exercise interventions consisted of four physical education lessons per week, focusing on either aerobic or cognitively demanding exercise and were compared with a control group that followed their regular physical education program of two lessons per week. White matter microstructure was assessed using diffusion tensor imaging in combination with tract‐based spatial statistics. Independent component analysis was performed on resting‐state data to identify RSNs. Furthermore, neurocognitive functioning (information processing and attention, working memory, motor response inhibition, interference control) was assessed by a set of computerized tasks. Results indicated no Group × Time effects on white matter microstructure or RSN activity, indicating no effects of the exercise interventions on these aspects of brain structure and function. Likewise, no Group × Time effects were found for neurocognitive performance. This study indicated that 14‐week school‐based interventions regarding neither aerobic exercise nor cognitive‐demanding exercise interventions influence brain structure and brain function in healthy children. This study was registered in the Netherlands Trial Register (NTR5341).

Differential age-dependent development of inter-area brain connectivity in term and preterm neonates

BackgroundAmong preterm infants, higher morbidities of neurological disturbances and developmental delays are critical issues. Resting-state networks (RSNs) in the brain are suitable measures for assessing higher-level neurocognition. Since investigating task-related brain activity is difficult in neonates, assessment of RSNs provides invaluable insight into their neurocognitive development.MethodsThe participants, 32 term and 71 preterm neonates, were divided into three groups based on gestational age (GA) at birth. Cerebral hemodynamic activity of RSNs was measured using functional near-infrared spectroscopy in the temporal, frontal, and parietal regions.ResultsHigh-GA preterm infants (GA ≥ 30 weeks) had a significantly stronger RSN than low-GA preterm infants and term infants. Regression analyses of RSNs as a function of postnatal age (PNA) revealed a steeper regression line in the high-GA preterm and term infants than in the low-GA infants, particularly for inter-area brain connectivity between the frontal and left temporal areas.ConclusionsSlower PNA-dependent development of the frontal–temporal network found only in the low-GA group suggests that significant brain growth optimal in the intrauterine environment takes place before 30 weeks of gestation. The present study suggests a likely reason for the high incidence of neurodevelopmental impairment in early preterm infants.ImpactResting-state fNIRS measurements in three neonate groups differing in gestational age (GA) showed stronger networks in the high-GA preterm infants than in the term and low-GA infants, which was partly explained by postnatal age (PNA).Regression analyses revealed a similar PNA-dependence in the development of the inter-area networks in the frontal and temporal lobes in the high-GA and term infants, and significantly slower development in the low-GA infants.These results suggest that optimal intrauterine brain growth takes place before 30 weeks of gestation. This explains one of the reasons for the high incidence of neurodevelopmental impairment in early preterm infants.

Shared genetic influences on resting-state functional networks of the brain.

The amplitude of activation in brain resting state networks (RSNs), measured with resting‐state functional magnetic resonance imaging, is heritable and genetically correlated across RSNs, indicating pleiotropy. Recent univariate genome‐wide association studies (GWASs) explored the genetic underpinnings of individual variation in RSN activity. Yet univariate genomic analyses do not describe the pleiotropic nature of RSNs. In this study, we used a novel multivariate method called genomic structural equation modeling to model latent factors that capture the shared genomic influence on RSNs and to identify single nucleotide polymorphisms (SNPs) and genes driving this pleiotropy. Using summary statistics from GWAS of 21 RSNs reported in UK Biobank (N = 31,688), the genomic latent factor analysis was first conducted in a discovery sample (N = 21,081), and then tested in an independent sample from the same cohort (N = 10,607). In the discovery sample, we show that the genetic organization of RSNs can be best explained by two distinct but correlated genetic factors that divide multimodal association networks and sensory networks. Eleven of the 17 factor loadings were replicated in the independent sample. With the multivariate GWAS, we found and replicated nine independent SNPs associated with the joint architecture of RSNs. Further, by combining the discovery and replication samples, we discovered additional SNP and gene associations with the two factors of RSN amplitude. We conclude that modeling the genetic effects on brain function in a multivariate way is a powerful approach to learn more about the biological mechanisms involved in brain function.

Decreased salience network fMRI functional connectivity following a course of rTMS for treatment-resistant depression

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is a treatment shown to be effective in treating major depressive disorder (MDD). However, the effect of rTMS therapy on functional connectivity within the brains of patients being treated for MDD remains poorly understood. Few studies have investigated the effects of a course of rTMS on resting-state network activity. MethodsIn an open-label naturalistic study, resting-state fMRI was collected prior to and following a four-week course of rTMS in 24 participants with MDD and 2 with bipolar disorder. Montgomery-Asberg depression rating scale scores showed a response rate of 42%. ResultsClinical response to rTMS was correlated with reduced functional connectivity from baseline to post-rTMS within the salience network (SN). This indicates SN connectivity may be functionally relevant to how rTMS produces antidepressant effects. In an exploratory inter-network analysis, connectivity between the SN and posterior default mode network (pDMN) was higher following treatment. However this difference was not correlated with the antidepressant response. Local BOLD activity within these networks was also assessed using the fractional amplitude of low-frequency fluctuations (fALFF) technique. Local activity increased in both the SN and pDMN following rTMS. However this increase was also not correlated with antidepressant response. LimitationsThe sample population was heterogeneous, continuing current use of medications, and the study lacked a healthy control or sham stimulation comparison group. ConclusionsTogether, these results provide evidence for the involvement of the SN in the antidepressant response to rTMS treatment.

Multimodal Imaging Brain Markers in Early Adolescence Are Linked with a Physically Active Lifestyle.

The World Health Organization promotes physical exercise and a healthy lifestyle as means to improve youth development. However, relationships between physical lifestyle and human brain development are not fully understood. Here, we asked whether a human brain–physical latent mode of covariation underpins the relationship between physical activity, fitness, and physical health measures with multimodal neuroimaging markers. In 50 12-year old school pupils (26 females), we acquired multimodal whole-brain MRI, characterizing brain structure, microstructure, function, myelin content, and blood perfusion. We also acquired physical variables measuring objective fitness levels, 7 d physical activity, body mass index, heart rate, and blood pressure. Using canonical correlation analysis, we unravel a latent mode of brain–physical covariation, independent of demographics, school, or socioeconomic status. We show that MRI metrics with greater involvement in this mode also showed spatially extended patterns across the brain. Specifically, global patterns of greater gray matter perfusion, volume, cortical surface area, greater white matter extra-neurite density, and resting state networks activity covaried positively with measures reflecting a physically active phenotype (high fit, low sedentary individuals). Showing that a physically active lifestyle is linked with systems-level brain MRI metrics, these results suggest widespread associations relating to several biological processes. These results support the notion of close brain-body relationships and underline the importance of investigating modifiable lifestyle factors not only for physical health but also for brain health early in adolescence.SIGNIFICANCE STATEMENT An active lifestyle is key for healthy development. In this work, we answer the following question: How do brain neuroimaging markers relate with young adolescents' level of physical activity, fitness, and physical health? Combining advanced whole-brain multimodal MRI metrics with computational approaches, we show a robust relationship between physically active lifestyles and spatially extended, multimodal brain imaging-derived phenotypes. Suggesting a wider effect on brain neuroimaging metrics than previously thought, this work underlies the importance of studying physical lifestyle, as well as other brain–body relationships in an effort to foster brain health at this crucial stage in development.

Resting state networks activity in euthymic bipolar disorder.

Bipolar disorder (BD) is a psychiatric condition causing shifts in mood, energy and activity levels severely altering the quality of life of the patients even in the euthymic phase. Although widely accepted, the neurobiological bases of the disorder in the euthymic phase remain elusive. This study aims at characterizing resting state functional activity of the BD euthymic phase in order to better understand the pathogenesis of the disease and build future neurobiological models. Fifteen euthymic BD patients (10 females; mean age 40.2; standard deviation 13.5; range 20-61) and 27 healthy controls (HC) (21 females; mean age 37; standard deviation 10.6; range 22-60) underwent a 3T functional MRI scan at rest. Resting state activity was extracted through independent component analysis (ICA) run with automatic dimensionality estimation. ICA identified 22 resting state networks (RSNs). Within-network analysis revealed decreased connectivity in the visual, temporal, motor and cerebellar RSNs of BD patients vs HC. Between-network analysis showed increased connectivity between motor area and the default mode network (DMN) partially overlapping with the fronto-parietal network (FPN) in BD patients. Within-network analysis confirmed existing evidence of altered cerebellar, temporal, motor and visual networks in BD. Increased connectivity between the DMN and the motor area network suggests the presence of alterations of the fronto-parietal regions, precuneus and cingulate cortex in the euthymic condition. These findings indicate that specific connectivity alterations might persist even in the euthymic state suggesting the importance of examining both within and between-network connectivity to achieve a global understanding of the BD euthymic condition.

Over-expression of miR-34a induces rapid cognitive impairment and Alzheimer’s disease-like pathology

Autosomal dominant Alzheimer disease (AD) is caused by rare mutations in one of three specific genes. This is in contrast to idiopathic, late-onset AD (LOAD), which has a more polygenetic risk profile and represents more than 95% of cases. Previously, we have demonstrated that increased expression of microRNA (miRNA)-34a (miR-34a) in AD brain targets genes linked to synaptic plasticity, energy metabolism, and resting state network activity. Here we report the generation of a heterozygous, conditional miR-34a overexpression mouse (miR-34a+/−(TetR-TetO-miR-34a) Transgenic Mice). Doxycycline-treated mice of either sex exhibited profound behavioral impairment compared to untreated groups with only 1–2 months of over-expression of miR-34a. Cognitive impairment of individual mice in T- and Y-maze tasks correlated with elevated miR-34a expression in many parts of the brain including the hippocampus and prefrontal cortex, regions which are known to be involved in this task and implicated in LOAD dysfunction. Immunocytochemistry of brain sections from mice show high amyloid β and phosphorylated tau-specific staining in the hippocampus and cortex. Analysis of protein samples from these mice revealed that miR-34a targets specific genes involved in memory formation, amyloid precursor protein (APP) metabolism and phosphorylation-dephosphorylation of tau. Thus, our results suggest that the polygenetic dysfunction caused by miR-34a may occur in LOAD and disclose miR-34a as a potential therapeutic target. Significance StatementLate-onset Alzheimer disease (LOAD) is associated with multiple gene alleles, a polygenetic profile of risk factors that is difficult to model in animals. Our approach to modeling LOAD was to produce a conditional over-expressing, miR-34a mouse using doxycycline-induction to activate expression. We observed that miR-34a over-expression results in a rapid cognitive impairment, associated with accumulation of intracellular Aβ and tau hyperphosphorylation in multiple brain regions. Targets for miR-34a, including ADAM10, NMDAR 2B, and SIRT1 RNAs, were profoundly reduced by miR-34a over-expression. Collectively, these results indicate that a rapid, profound cognitive decline and Alzheimer’s disease neuropathology can be induced with miR-34a over-expression, suggesting that this animal model may represent a polygenetic risk factor model for LOAD.

Imagery-Mediated Verbal Learning Depends on Vividness-Familiarity Interactions: The Possible Role of Dualistic Resting State Network Activity Interference.

Using secondary database analysis, we tested whether the (implicit) familiarity of eliciting noun-cues and the (explicit) vividness of corresponding imagery exerted additive or interactive influences on verbal learning, as measured by the probability of incidental noun recall and image latency times (RTs). Noun-cues with incongruent levels of vividness and familiarity (high/low; low/high, respectively) at encoding were subsequently associated at retrieval with the lowest recall probabilities, while noun-cues related with congruent levels (high/high; low/low) were associated with higher recall probabilities. RTs in the high vividness and high familiarity grouping were significantly faster than all other subsets (low/low, low/high, high/low) which did not significantly differ among each other. The findings contradict: (1) associative theories predicting positive monotonic relationships between memory strength and learning; and (2) non-monotonic plasticity hypothesis (NMPH), aiming at generalizing the non-monotonic relationship between a neuron’s excitation level and its synaptic strength to broad neural networks. We propose a dualistic neuropsychological model of memory consolidation that mimics the global activity in two large resting-state networks (RSNs), the default mode network (DMN) and the task-positive-network (TPN). Based on this model, we suggest that incongruence and congruence between vividness and familiarity reflect, respectively, competition and synergy between DMN and TPN activity. We argue that competition or synergy between these RSNs at the time of stimulus encoding disproportionately influences long term semantic memory consolidation in healthy controls. These findings could assist in developing neurophenomenological markers of core memory deficits currently hypothesized to be shared across multiple psychopathological conditions.

Potential Intra- or Cross-Network Functional Reorganization of the Triple Unifying Networks in Patients with Frontal Glioma

Patients with frontal glioma might experience cognition alterations together with potential dysfunction of resting-state networks (RSNs). To understand the altered patterns of the intrinsic activity and underlying network interactions between the cognitive-related RSNs is of great importance. This study aimed to investigate the characteristics of the altered RSNs, including default mode network, executive control network, and salience network and further elucidate the possible functional reorganization of RSNs in patients with frontal glioma. Thirteen patients with frontal glioma and 10 healthy controls (HCs) were enrolled in this study. Independent component analysis was performed to identify the characteristics of the selected RSNs activity. Further, functional connectivity analysis was applied to investigate the relationship between the altered regions. Subsequently, partial correlation analysis was performed to examine associations between the neural activity of RSNs and neurocognitive characteristics. Compared with the HCs group, the patient group exhibited significant differences in functional connectivity among default mode network, executive control network, and salience network. In addition, the number of the significant functional connectivities between the paired seeds observed in the patients was greater than that in HCs and significantly increased functional connectivity was detected between left posterior cingulate cortex and right angular gyrus. Furthermore, altered neural activities in the RSNs of patients with frontal glioma were positively associated with certain aspects of cognitive function. Our results suggested underlying network functional reorganization of the triple unifying RSNs in patients with frontal glioma, providing novel insights for improving understanding of brain function.

Opposite effects of dopamine and serotonin on resting-state networks: review and implications for psychiatric disorders.

Alterations in brain intrinsic activity-as organized in resting-state networks (RSNs) such as sensorimotor network (SMN), salience network (SN), and default-mode network (DMN)-and in neurotransmitters signaling-such as dopamine (DA) and serotonin (5-HT)-have been independently detected in psychiatric disorders like bipolar disorder and schizophrenia. Thus, the aim of this work was to investigate the relationship between such neurotransmitters and RSNs in healthy, by reviewing the relevant work on this topic and performing complementary analyses, in order to better understand their physiological link, as well as their alterations in psychiatric disorders. According to the reviewed data, neurotransmitters nuclei diffusively project to subcortical and cortical regions of RSNs. In particular, the dopaminergic substantia nigra (SNc)-related nigrostriatal pathway is structurally and functionally connected with core regions of the SMN, whereas the ventral tegmental area (VTA)-related mesocorticolimbic pathway with core regions of the SN. The serotonergic raphe nuclei (RNi) connections involve regions of the SMN and DMN. Coherently, changes in neurotransmitters activity impact the functional configuration and level of activity of RSNs, as measured by functional connectivity (FC) and amplitude of low-frequency fluctuations/temporal variability of BOLD signal. Specifically, DA signaling is associated with increase in FC and activity in the SMN (hypothetically via the SNc-related nigrostriatal pathway) and SN (hypothetically via the VTA-related mesocorticolimbic pathway), as well as concurrent decrease in FC and activity in the DMN. By contrast, 5-HT signaling (via the RNi-related pathways) is associated with decrease in SMN activity along with increase in DMN activity. Complementally, our empirical data showed a positive correlation between SNc-related FC and SMN activity, whereas a negative correlation between RNi-related FC and SMN activity (along with tilting of networks balance toward the DMN). According to these data, we hypothesize that the activity of neurotransmitter-related neurons synchronize the low-frequency oscillations within different RSNs regions, thus affecting the baseline level of RSNs activity and their balancing. In our model, DA signaling favors the predominance of SMN-SN activity, whereas 5-HT signaling favors the predominance of DMN activity, manifesting in distinct behavioral patterns. In turn, alterations in neurotransmitters signaling (or its disconnection) may favor a correspondent functional reorganization of RSNs, manifesting in distinct psychopathological states. The here suggested model carries important implications for psychiatric disorders, providing novel and well testable hypotheses especially on bipolar disorder and schizophrenia.

Neuronal dynamics enable the functional differentiation of resting state networks in the human brain.

Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals. Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders.