Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks

In the human brain, default mode network BOLD deactivations can be accompanied by both increases and decreases in glucose metabolism, depending on the respective metabolic demands of task-positive cognitive control and attention networks.

Attentional processing has been associated with the dorsal attention, default mode, and fronto-parietal control networks. The dorsal attention network is involved in externally focused attention whereas the default mode network is involved in internally directed attention. The fronto-parietal control network has been proposed to mediate the transition between external and internal attention by coupling its activity to either the dorsal attention network or the default mode network depending on the attentional demand. Dopamine is hypothesized to modulate attention and has been linked to the integrity of these three attention-related networks. We used positron emission tomography (PET) with 6-[18F]fluoro-L-m-tyrosine to quantify dopamine synthesis capacity in vivo and functional magnetic resonance imaging (fMRI) to acquire stimulus-independent brain activity in cognitively healthy human subjects. We found that in the resting state where internal cognition dominates, dopamine enhances the coupling between the fronto-parietal control network and the default mode network while reducing the coupling between the fronto-parietal control network and the dorsal attention network. These results add a neurochemical perspective to the role of network interaction in modulating attention.

One of the great frontiers of consciousness science is understanding how early consciousness arises in the development of the human infant. The reciprocal relationship between the default mode network and fronto-parietal networks—the dorsal attention and executive control network—is thought to facilitate integration of information across the brain and its availability for a wide set of conscious mental operations. It remains unknown whether the brain mechanism of conscious awareness is instantiated in infants from birth. To address this gap, we investigated the development of the default mode and fronto-parietal networks and of their reciprocal relationship in neonates. To understand the effect of early neonate age on these networks, we also assessed neonates born prematurely or before term-equivalent age. We used the Developing Human Connectome Project, a unique Open Science dataset which provides a large sample of neonatal functional MRI data with high temporal and spatial resolution. Resting state functional MRI data for full-term neonates (n = 282, age 41.2 weeks ± 12 days) and preterm neonates scanned at term-equivalent age (n = 73, 40.9 weeks ± 14.5 days), or before term-equivalent age (n = 73, 34.6 weeks ± 13.4 days), were obtained from the Developing Human Connectome Project, and for a reference adult group (n = 176, 22–36 years), from the Human Connectome Project. For the first time, we show that the reciprocal relationship between the default mode and dorsal attention network was present at full-term birth or term-equivalent age. Although different from the adult networks, the default mode, dorsal attention and executive control networks were present as distinct networks at full-term birth or term-equivalent age, but premature birth was associated with network disruption. By contrast, neonates before term-equivalent age showed dramatic underdevelopment of high-order networks. Only the dorsal attention network was present as a distinct network and the reciprocal network relationship was not yet formed. Our results suggest that, at full-term birth or by term-equivalent age, infants possess key features of the neural circuitry that enables integration of information across diverse sensory and high-order functional modules, giving rise to conscious awareness. Conversely, they suggest that this brain infrastructure is not present before infants reach term-equivalent age. These findings improve understanding of the ontogeny of high-order network dynamics that support conscious awareness and of their disruption by premature birth.

BackgroundMenstrually-related migraine (MRM) is a subtype of migraine associated with the ovarian cycle that imposes a significant burden on female patients. Although MRM and non-menstrual migraine (NMM) differ in clinical presentation and treatment response, their distinct neural mechanisms remain unclear. Emerging evidence suggests that alterations in intrinsic functional connectivity (FC) within and between large-scale brain networks may underlie the phenotypic heterogeneity of migraine subtypes. This study investigated FC alterations between patients with MRM and NMM, explored their correlations with clinical characteristics, and assessed the preliminary utility of FC in subtype differentiation.MethodsResting-state functional magnetic resonance imaging (MRI) with independent component analysis was used to examine whole-brain FC in 50 patients with MRM, 50 with NMM and 50 age-balanced healthy controls (HC). We analyzed within- and between-network connectivity across major resting-state networks, including the frontoparietal, default mode, salience and dorsal attention networks, and applied logistic regression to test whether FC values could classify migraine subtypes. Correlation analyses were further performed between FC measures and clinical indices, including disease duration, headache frequency, visual analog scale scores and Headache Impact Test (HIT-6) scores.ResultsBoth MRM and NMM groups showed weaker within-network connectivity compared to HCs, primarily in the right frontoparietal, default mode and salience networks. Compared with NMM, the MRM group exhibited significantly stronger connectivity in the left frontoparietal network and weaker between-network connectivity between the dorsal attention and default mode networks. In the women with migraine, FC within the dorsal attention network (DAN) was negatively correlated with disease duration (r = -0.200, p = 0.046) and HIT-6 score (r = -0.183, p = 0.049). Furthermore, FC between the DAN and auditory network was inversely associated with disease duration (r = -0.225, p = 0.025). The logistic regression model achieved an area under the receiver operating characteristic curve of 0.73 (sensitivity = 0.70; specificity = 0.64) in distinguishing MRM from NMM.ConclusionsOur findings reveal both shared and distinct alterations in large-scale brain networks in MRM and NMM, potentially explaining differences in clinical presentation and treatment response. This enhanced understanding of migraine pathophysiology supports the development of subtype-specific diagnostic tools and targeted therapies and underscores the value of resting-state fMRI as a non-invasive tool for migraine phenotyping and personalized care.Registration NumberChiCTR2200065586.

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

Introduction Spleen deficiency syndrome (SDS), a common clinical syndrome of traditional Chinese medicine, is manifested with digestive symptoms and cognitive impairments. However, the cognitive neural mechanism in brain networks of SDS still remained unclear. Our aim was to investigate the changes between the default mode, dorsal attention, and frontoparietal networks in SDS. Methods Twenty nonorganic gastrointestinal disorder (NOGD) patients with SDS and eighteen healthy controls were enrolled to attend functional magnetic resonance imaging scan and participated a continuous performance test (CPT) before scanning. Results Compared with healthy controls, NOGD patients with SDS showed the significantly increased functional connectivity (FC) between dorsal attention network (DAN) and left frontal-parietal control network (LFPN) and significantly decreased FC between LFPN and default mode network (DMN). The functional network connectivity analysis showed positive correlation coefficients between the DAN and LFPN and DAN and DMN as well as negative correlation between LFPN and DMN in NOGD patients with SDS compared with healthy controls. Correlation analysis revealed that the increased FC between LFPN and DAN was positively correlated with 4-digitnumber reaction time mean (RTM) and 3-digitnumber RTM. Conclusion Our study may provide novel insights into the relationship among the DMN, DAN, and FPN in NOGD patients with SDS to deepen our understanding of the neuropsychological mechanisms of SDS.

Default network activity, coupled with the frontoparietal control network, supports goal-directed cognition

Congenital heart disease (CHD) has been associated with structural brain growth and long-term developmental impairments, including deficits in learning, memory, and executive functions. Altered functional connectivity has been shown to be altered in neonates born with CHD; however, it is unclear if these early life alterations are also present during adulthood. Therefore, this study aimed to compare resting state functional connectivity networks associated with executive function deficits between youth (16 to 24 years old) with complex CHD (mean age = 20.13; SD = 2.35) who underwent open-heart surgery during infancy and age- and sex-matched controls (mean age = 20.41; SD = 2.05). Using the Behavior Rating Inventory of Executive Function–Adult Version questionnaire, we found that participants with CHD presented with poorer performance on the inhibit, initiate, emotional control, working memory, self-monitor, and organization of materials clinical scales than healthy controls. We then compared the resting state networks theoretically corresponding to these impaired functions, namely the default mode, dorsal attention, fronto-parietal, fronto-orbital, and amygdalar networks, between the two groups. Participants with CHD presented with decreased functional connectivity between the fronto-orbital cortex and the hippocampal regions and between the amygdala and the frontal pole. Increased functional connectivity was observed within the default mode network, the dorsal attention network, and the fronto-parietal network. Overall, our results suggest that youth with CHD present with disrupted resting state functional connectivity in widespread networks and regions associated with altered executive functioning.

PurposeConventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer’s disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls.MethodsWe used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies–Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05.ResultsWe identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04).ConclusionWe show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.

BackgroundThe Useful Field of View (UFOV) test assesses divided attention/speed of processing and relates to independent activities of daily living. Cognitive training utilizing UFOV improves speed‐of‐processing and reduces dementia risk by 29%. Neural correlates of UFOV include key regions of “higher‐order” resting state networks (RSNs) involved in attention processing and dementia risk: Frontoparietal control network (FPCN), Cingulo Opercular network (CON), Default Mode network (DMN), and Dorsal Attention network (DAN). Reduced anti‐correlation between the DMN and task positive RSNs (FPCN, CON, and DAN) is reported in dementia progression. Assessing the relationship of UFOV performance with within‐ and between‐ RSN connectivity may suggest neural mechanisms of cognitive training and potential pathways for reduced dementia risk. We hypothesize that FPCN, CON, and DAN within network connectivity will predict UFOV performance, as these networks are involved in attention processing. We also predict that DMN‐CON anti‐correlation will predict UFOV performance, as this anti‐correlation is related to dementia progression.Method279 healthy older adults (M = 71.6, SD = 5.1) from a larger multi‐site clinical trial were recruited. UFOV performance was measured via Double Decision (DD) task from Posit Science Brain HQ. Participants correctly identified a central target, while simultaneously specifying location of a peripheral target among distractors. Average within‐network connectivity of FPCN, CON, DAN, and DMN, and between‐network connectivity of DMN‐CON, DMN‐FPCN, and DMN‐DAN were extracted from resting‐state functional magnetic resonance imaging through the CONN Toolbox v18b via SPM 12. Multiple linear regressions predicted DD performance from within‐ and between‐network values, controlling for age, sex, education, and scanner. All between‐network values were included in the same regression model.ResultReduced CON (β=‐.134, p=.023) and FPCN (β=‐.126, p=.029) connectivity was associated with poorer DD performance. Less DMN‐CON anticorrelation demonstrated a trend for predicting better DD performance (β=‐.126, p=.060).ConclusionConsistent with previous research, faster DD performance is associated with RSNs involved in executive functioning, attention, and processing speed. Although only trending, reduced DMN‐CON anticorrelation may be related to DD performance. This suggests that within network connectivity may contribute more to DD performance than between‐network connectivity involved in dementia progression in healthy older adults.

目的 探讨精神分裂症患者脑静息态默认网络、中心执行网络及突显网络的网络内及网络间的功能连接特点.方法 对74例精神分裂症患者(患者组)和79名相匹配的健康者(对照组)进行静息态fMRI扫描.采取功能连接分析方法,以内侧前额叶皮质及右侧前岛叶为感兴趣区域分别提取默认网络、中心执行网络及突显网络,分析3个网络内及网络间静息态功能连接情况;并对有差异的功能连接与精神症状及病程等进行偏相关分析.结果 与对照组相比,患者组内侧前额叶皮质与双侧背外侧前额叶皮质(左侧-0.02±0.18与-0.12±0.12,t=4.25,P＜0.01;右侧-0.02±0.11与-0.10±0.10,t=5.40,P＜0.01)、双侧豆状壳核(左侧0.14±0.12与0.04±0.10,t=5.03,P＜0.01;右侧0.13±0.11与0.03±0.98,t=4.96,P＜0.01)的功能连接增强;右侧前岛叶与左侧额中回(-0.05±0.17与-0.07±0.17,t=4.60,P＜0.01)及楔前叶/后扣带回(0.01±0.13与-0.09±0.14,t=4.01,P＜0.01)的功能连接增强(均采用高斯随机场理论校正,体素水平P＜0.01,簇水平P＜0.05);默认网络与中心执行网络、突显网络间的负向功能连接减小甚至消失(Bonferroni校正P＜0.05).患者组PANSS阳性症状分与右侧前岛叶与楔前叶/后扣带回皮质间的功能连接呈正相关(r=0.232,P=0.025);患者组病程与右侧前岛叶与左侧额中回(r=0.288,P=0.007)、楔前叶/后扣带回(r=0.196,P=0.049)间的功能连接呈正相关.结论 精神分裂症患者存在静息态默认网络、中心执行网络及突显网络3个网络间的连接异常,可能与精神分裂症的病理机制有关.

Cognition in ESRD may be improved by kidney transplantation, but mechanisms are unclear. We explored patterns of resting-state networks with resting-state functional magnetic resonance imaging among patients with ESRD before and after kidney transplantation. Thirty-seven patients with ESRD scheduled for kidney transplantation and 22 age-, sex-, and education-matched healthy subjects underwent resting-state functional magnetic resonance imaging. Patients were imaged before and 1 and 6 months after kidney transplantation. Functional connectivity of seven resting-state subnetworks was evaluated: default mode network, dorsal attention network, central executive network, self-referential network, sensorimotor network, visual network, and auditory network. Mixed effects models tested associations of ESRD, kidney transplantation, and neuropsychological measurements with functional connectivity. Compared with controls, pretransplant patients showed abnormal functional connectivity in six subnetworks. Compared with pretransplant patients, increased functional connectivity was observed in the default mode network, the dorsal attention network, the central executive network, the sensorimotor network, the auditory network, and the visual network 1 and 6 months after kidney transplantation (P=0.01). Six months after kidney transplantation, no significant difference in functional connectivity was observed for the dorsal attention network, the central executive network, the auditory network, or the visual network between patients and controls. Default mode network and sensorimotor network remained significantly different from those in controls when assessed 6 months after kidney transplantation. A relationship between functional connectivity and neuropsychological measurements was found in specific brain regions of some brain networks. The recovery patterns of resting-state subnetworks vary after kidney transplantation. The dorsal attention network, the central executive network, the auditory network, and the visual network recovered to normal levels, whereas the default mode network and the sensorimotor network did not recover completely 6 months after kidney transplantation. Neural resting-state functional connectivity was lower among patients with ESRD compared with control subjects, but it significantly improved with kidney transplantation. Resting-state subnetworks exhibited variable recovery, in some cases to levels that were no longer significantly different from those of normal controls.

There is no diagnostic assessment procedure with moderate or strong evidence of use, and evidence for current means of treating prolonged disorders of consciousness (pDOC) is sparse. This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough. Therefore, the aim of this study was to explore the mechanism of pDOC using functional near-infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for determining the arousal of pDOC patients. Five minutes resting-state data were collected from 10 pDOC patients and 13healthy adults using fNIRS. Based on the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the time series, the resting-state cortical brain functional connectivity strengths of the two groups were calculated, and the functional connectivity strengths of homologous and heterologous brain networks were compared at the sensorimotor network (SEN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), and visual network (VIS) levels. Univariate binary logistic regression analyses were performed on brain networks with statistically significant differences to identify brain networks associated with arousal in pDOC patients. The receiver operating characteristic (ROC) curves were further analyzed to determine the cut-off value of the relevant brain networks to provide clinical biomarkers for the prediction of arousal in pDOC patients. The results showed that the functional connectivity strengths of oxyhemoglobin (HbO)-based SEN∼SEN, VIS∼VIS, DAN∼DAN, DMN∼DMN, SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, VIS∼DMN, HbR-based SEN∼SEN, and SEN∼DAN were significantly reduced in the pDOC group and were factors that could reflect the participants' state of consciousness. The cut-off value of resting-state functional connectivity strength calculated by ROC curve analysis can be used as a potential preclinical marker for predicting the arousal state of subjects. Resting-state functional connectivity strength of cortical networks is significantly reduced in pDOC patients. The cut-off values of resting-state functional connectivity strength are potential preclinical markers for predicting arousal in pDOC patients.

Sleep deprivation (SD) is very common in modern society and has a profound effect on cognitive function, in particular on working memory (WM). This type of memory is required for completion of many tasks and is adversely affected by SD. However, the cognitive neural mechanism by which SD affects WM, remains unclear. In this study, we investigated the changes in the brain network involved in WM after SD. Twenty-two healthy subjects underwent functional magnetic resonance imaging scan while in a state of resting wakefulness and again after 36 h of total SD and performed a WM task before each scanning session. Nineteen main nodes of the default mode network (DMN), dorsal attention network (DAN), fronto-parietal network (FPN), salience network (SN), and other networks were selected for functional analysis of brain network connections. Functional connectivity measures were computed between seed areas for region of interest (ROI)-to-ROI analysis and to identify patterns of ROI-to-ROI connectivity. The relationship between the significant changes in functional connectivity in the brain network and WM performance were then examined by Pearson’s correlation analysis. WM performance declined significantly after SD. Compared with the awake state, the functional connectivity between DAN and DMN significantly increased after SD while that between FPN and DMN significantly decreased. Correlation analysis showed that the enhanced functional connectivity between DAN and DMN was negatively correlated with the decline in WM performance and that the decline in functional connectivity between FPN and DMN was positively correlated with decreased WM performance. These findings suggested that SD may affect WM by altering the functional connectivity among DMN, DAN, and FPN.

Abnormal resting-state functional connectivity in adolescent depressive episodes.