Default heterogeneity: from a singular construct to multiple default network(s)

Fibromyalgia (FM) is considered to be the prototypical central chronic pain syndrome and is associated with widespread pain that fluctuates spontaneously. Multiple studies have demonstrated altered brain activity in these patients. The objective of this study was to investigate the degree of connectivity between multiple brain networks in patients with FM, as well as how activity in these networks correlates with the level of spontaneous pain. Resting-state functional magnetic resonance imaging (FMRI) data from 18 patients with FM and 18 age-matched healthy control subjects were analyzed using dual-regression independent components analysis, which is a data-driven approach for the identification of independent brain networks. Intrinsic, or resting-state, connectivity was evaluated in multiple brain networks: the default mode network (DMN), the executive attention network (EAN), and the medial visual network (MVN), with the MVN serving as a negative control. Spontaneous pain levels were also analyzed for covariance with intrinsic connectivity. Patients with FM had greater connectivity within the DMN and right EAN (corrected P [P(corr)] < 0.05 versus controls), and greater connectivity between the DMN and the insular cortex, which is a brain region known to process evoked pain. Furthermore, greater intensity of spontaneous pain at the time of the FMRI scan correlated with greater intrinsic connectivity between the insula and both the DMN and right EAN (P(corr) < 0.05). These findings indicate that resting brain activity within multiple networks is associated with spontaneous clinical pain in patients with FM. These findings may also have broader implications for how subjective experiences such as pain arise from a complex interplay among multiple brain networks.

Disruption of Anterior Insula Modulation of Large-Scale Brain Networks in Schizophrenia

Individuals with chronic traumatic brain injury (TBI) often show detrimental deficits in higher order cognitive functions requiring coordination of multiple brain networks. Although assessing TBI-related deficits in higher order cognition in the context of network dysfunction is promising, few studies have systematically investigated altered interactions among multiple networks in chronic TBI. We characterized disrupted resting-state functional connectivity of the default mode network (DMN), dorsal attention network (DAN), and frontoparietal control network (FPCN) whose interactions are required for internally and externally focused goal-directed cognition in chronic TBI. Specifically, we compared the network interactions of 40 chronic TBI individuals (8 years post-injury on average) with those of 17 healthy individuals matched for gender, age, and years of education. The network-based statistic (NBS) on DMN-DAN-FPCN connectivity of these groups revealed statistically significant (p NBS2.58) reductions in within-DMN, within-FPCN, DMN-DAN, and DMN-FPCN connectivity of the TBI group over healthy controls. Importantly, such disruptions occurred prominently in between-network connectivity. Subsequent analyses further exhibited the disrupted connectivity patterns of the chronic TBI group occurring preferentially in long-range and inter-hemispheric connectivity of DMN-DAN-FPCN. Most importantly, graph-theoretic analysis demonstrated relative reductions in global, local and cost efficiency (p<.05) as a consequence of the network disruption patterns in the TBI group. Our findings suggest that assessing multiple networks-of-interest simultaneously will allow us to better understand deficits in goal-directed cognition and other higher order cognitive phenomena in chronic TBI. Future research will be needed to better understand the behavioral consequences related to these network disruptions.

Higher functional connectivity (FC) in resting-state networks has been shown in individuals at risk of Alzheimer's disease (AD) by many studies. However, the longitudinal trajectories of the FC remain unknown. The present 35-month follow-up study aimed to explore longitudinal changes in higher FC in multiple resting-state networks in subjects with the apolipoprotein E ε4 allele (ApoE4) and/or amnestic mild cognitive impairment (aMCI). Fifty-one subjects with aMCI and 64 cognitively normal (CN) subjects underwent neuropsychological tests and resting-state functional magnetic resonance imaging (fMRI) scans twice from April 2011 to June 2015. Subjects were divided into 4 groups according to diagnosis and ApoE4 status. The CN non-ApoE4 group served as a control group, and other groups served as AD risk groups. The cross-sectional and longitudinal patterns of multiple resting-state networks, including default mode network, hippocampus network, executive control network, and salience network, were explored by comparing FC data between groups and between time points, respectively. At baseline, compared with the control group, the AD risk groups showed higher FC with 8 regions in multiple networks. At follow-up, 6 of the regions displayed longitudinally decreased FC in AD risk groups. In contrast, the FC with all of these regions was maintained in the control group. Notably, among the 3 risk groups, most of the higher FC at baseline (5 of the 8 regions) and longitudinally decreased FC at follow-up (4 of the 6 regions) were shown in the aMCI ApoE4 group. Higher resting-state FC is followed by a decline in subjects at AD risk, and this inverse U-shaped trajectory is more notable in subjects with higher risk.

Failure to integrate: Connector hub dysfunction in major depressive disorder

Abnormal resting-state functional connectivity in adolescent depressive episodes.

Altered Intrinsic Functional Brain Architecture in Children at Familial Risk of Major Depression

In resting state fMRI (rs-fMRI), only functional connectivity (FC) reductions in the default mode network (DMN) are normally reported as a biomarker for Alzheimer's disease (AD). In this investigation we have developed a comprehensive strategy to characterize the FC changes occurring in multiple networks and applied it in a pilot study of subjects with AD and Mild Cognitive Impairment (MCI), compared to healthy controls (HC). Resting state networks (RSNs) were studied in 14 AD (70 ± 6 years), 12 MCI (74 ± 6 years), and 16 HC (69 ± 5 years). RSN alterations were present in almost all the 15 recognized RSNs; overall, 474 voxels presented a reduced FC in MCI and 1244 in AD while 1627 voxels showed an increased FC in MCI and 1711 in AD. The RSNs were then ranked according to the magnitude and extension of FC changes (gFC), putting in evidence 6 RSNs with prominent changes: DMN, frontal cortical network (FCN), lateral visual network (LVN), basal ganglia network (BGN), cerebellar network (CBLN), and the anterior insula network (AIN). Nodes, or hubs, showing alterations common to more than one RSN were mostly localized within the prefrontal cortex and the mesial-temporal cortex. The cerebellum showed a unique behavior where voxels of decreased gFC were only found in AD while a significant gFC increase was only found in MCI. The gFC alterations showed strong correlations (p < 0.001) with psychological scores, in particular Mini-Mental State Examination (MMSE) and attention/memory tasks. In conclusion, this analysis revealed that the DMN was affected by remarkable FC increases, that FC alterations extended over several RSNs, that derangement of functional relationships between multiple areas occurred already in the early stages of dementia. These results warrant future work to verify whether these represent compensatory mechanisms that exploit a pre-existing neural reserve through plasticity, which evolve in a state of lack of connectivity between different networks with the worsening of the pathology.

Neuroimaging studies of working memory training have identified the alteration of brain activity as well as the regional interactions within the functional networks such as central executive network (CEN) and default mode network (DMN). However, how the interaction within and between these multiple networks is modulated by the training remains unclear. In this paper, we examined the interaction of three training-induced brain networks during working memory training based on real-time functional magnetic resonance imaging (rtfMRI). Thirty subjects assigned to the experimental and control group respectively participated in two times training separated by seven days. Three networks including silence network (SN), CEN and DMN were identified by the training data with the calculated function connections within each network. Structural equation modeling (SEM) approach was used to construct the directional connectivity patterns. The results showed that the causal influences from the percent signal changes of target ROI to the SN were positively changed in both two groups, as well as the causal influence from the SN to CEN was positively changed in experimental group but negatively changed in control group from the SN to DMN. Further correlation analysis of the changes in each network with the behavioral improvements showed that the changes in SN were stronger positively correlated with the behavioral improvement of letter memory task. These findings indicated that the SN was not only a switch between the target ROI and the other networks in the feedback training but also an essential factor to the behavioral improvement.

BackgroundPeople with mild cognitive impairment (MCI) experience a loss of cognitive functions, whose mechanism is characterized by aberrant structure‒function (SC-FC) coupling and topological attributes of multiple networks. This study aimed to reveal the network-level SC-FC coupling and internal topological changes triggered by computerized cognitive training (CCT) to explain the therapeutic effects of this training in individuals with MCI.MethodsIn this randomized block experiment, we recruited 60 MCI individuals and randomly divided them into an 8-week multidomain CCT group and a health education control group. The neuropsychological outcome measures were the Montreal Cognitive Assessment (MoCA), Chinese Auditory Verbal Learning Test (CAVLT), Chinese Stroop Color–Word Test (SCWT), and Rey–Osterrieth Complex Figure Test (Rey CFT). The brain imaging outcome measures were SC-FC coupling and topological attributes using functional MRI and diffusion tensor imaging methods. We applied linear model analysis to assess the differences in the outcome measures and identify the correspondence between the changes in the brain networks and cognitive functions before and after the CCT.ResultsFifty participants were included in the analyses after the exclusion of three dropouts and seven participants with low-quality MRI scans. Significant group × time effects were found on the changes in the MoCA, CAVLT, and Rey CFT recall scores. The changes in the SC-FC coupling values of the default mode network (DMN) and somatomotor network (SOM) were higher in the CCT group than in the control group (P(unc.) = 0.033, P(unc.) = 0.019), but opposite effects were found on the coupling values of the visual network (VIS) (P(unc.) = 0.039). Increasing clustering coefficients in the functional DMN and SOM and subtle changes in the nodal degree centrality and nodal efficiency of the right dorsal medial prefrontal cortex, posterior cingulate cortex, left parietal lobe, somatomotor area, and visual cortex were observed in the CCT group (P < 0.05, Bonferroni correction). Significant correspondences were found between global cognitive function and DMN coupling values (P(unc.) = 0.007), between immediate memory and SOM as well as FPC coupling values (P(unc.) = 0.037, P(unc.) = 0.030), between delayed memory and SOM coupling values (P(unc.) = 0.030), and between visual memory and VIS coupling values (P(unc.) = 0.007).ConclusionsEight weeks of CCT effectively improved global cognitive and memory functions; these changes were correlated with increases in SC-FC coupling and changes in the topography of the DMN and SOM in individuals with MCI. The CCT regimen also modulated the clustering coefficient and the capacity for information transformation in functional networks; these effects appeared to underlie the cognitive improvement associated with CCT.Trial registrationChinese Clinical Trial Registry, ChiCTR2000034012. Registered on 21 June 2020.

FIGURE 1. Changes in cortical thickness provide one measure of brain maturation. A large longitudinal study found that for most areas of cortex, children with attention deficit hyperactivity disorder (ADHD) reach peak cortical thickness several years later than typically developing children, supporting presence of developmental delay. The rate of cortical thinning also differed between the group who continued to meet diagnostic criteria into adulthood (persistent ADHD) and those who did not (remitted ADHD). Areas of cortex in which the rate of thinning correlated with adult symptom level (green, more symptoms associated with more thinning) are approximated on medial and lateral simplified representations of cortex. An earlier study also identified multiple areas in which cortex was thinner in adults with persistent ADHD compared with controls (orange). In addition, this study noted some areas of thicker cortex in remitted ADHD when compared with persistent ADHD (blue).

"Superagers" are older adults who, despite their advanced age, maintain youthful memory. Previous morphometry studies revealed multiple default mode network (DMN) and salience network (SN) regions whose cortical thickness is greater in superagers and correlates with memory performance (Fig 1). In this study, we examined the intrinsic functional connectivity within DMN and SN in 41 young (24.5 ± 3.6 years old) and 40 older adults (66.9 ± 5.5 years old). Superaging was defined as youthful performance on a memory recall task, the California Verbal Learning Test (CVLT). Participants underwent a resting state fMRI scan and performed a separate visual-verbal recognition memory task. As predicted, within both DMN and SN, superagers had stronger connectivity compared to typical older adults and similar connectivity compared to young adults (Fig. 2). Superagers also performed similarly to young adults and better than typical older adults on the recognition task, demonstrating youthful episodic memory that generalized across memory tasks. Stronger connectivity within each network independently predicted better performance on both the CVLT and recognition task in older adults (Fig. 3 and Fig. 4). Variation in intrinsic connectivity explained unique variance in memory performance, above and beyond youthful neuroanatomy. A post-hoc analysis revealed that DMN and SN nodes were more strongly inversely correlated in superagers than in typical older adults but were similarly correlated in superagers and young adults. Stronger between-network inverse correlations also predicted better memory performance in the entire sample of older adults (Fig. 5).

fMRI studies typically explore changes in the BOLD signal underlying discrete cognitive processes that occur over milliseconds to a few seconds. However, autobiographical cognition is a protracted process and requires fMRI tasks with longer trials to capture the temporal dynamics of the underlying brain networks. In the current study, we provided an updated analysis of the fMRI data obtained from a published autobiographical event simulation study, with a slow event-related design (34-sec trials), that involved participants recalling past, imagining past, and imagining future autobiographical events, as well as completing a semantic association control task. Our updated analysis using Constrained Principal Component Analysis for fMRI retrieved two networks reported in the original study: (1) the default mode network, which activated during the autobiographical event simulation conditions but deactivated during the control condition, and (2) the multiple demand network, which activated early in all conditions during the construction of the required representations (i.e., autobiographical events or semantic associates). Two novel networks also emerged: (1) the Response Network, which activated during the scale-rating phase, and (2) the Maintaining Internal Attention Network, which, while active in all conditions during the elaboration of details associated with the simulated events, was more strongly engaged during the imagination and semantic association control conditions. Our findings suggest that the default mode network does not support autobiographical simulation alone, but it co-activates with the multiple demand network and Maintaining Internal Attention Network, with the timing of activations depending on evolving task demands during the simulation process.

Fluctuations of endogenous estrogen modulates fear extinction, but the influence of exogenous estradiol is less studied. Moreover, little focus has been placed on the impact of estradiol on broad network connectivity beyond the fear extinction circuit. Here, we examined the effect of acute exogenous estradiol administration on fear extinction-induced brain activation, whole-brain functional connectivity (FC) during the fear extinction task and post-extinction resting-state. Ninety healthy women (57 using oral contraceptives [OC], 33 naturally cycling [NC]) were fear conditioned on day 1. They ingested an estradiol or placebo pill prior to extinction learning on day 2 (double-blind design). Extinction memory was assessed on day 3. Task-based functional MRI data were ascertained on days 2 and 3 and resting-state data were collected post-extinction on day 2 and pre-recall on day 3. Estradiol administration significantly modulated the neural signature associated with fear extinction learning and memory, consistent with prior studies. Importantly, estradiol administration induced significant changes in FC within multiple networks, including the default mode and somatomotor networks during extinction learning, post-extinction, and during extinction memory recall. Exploratory analyses revealed that estradiol impacted ventromedial prefrontal cortex (vmPFC) activation and FC differently in the NC and OC women. The data implicate a more diffused and significant effect of acute estradiol administration on multiple networks. Such an effect might be beneficial to modulating attention and conscious processes in addition to engaging neural processes associated with emotional learning and memory consolidation.

The identification of a neurodegenerative disorder's distributed pattern of atrophy-or atrophy 'signature'-can lend insights into the cortical networks that degenerate in individuals with specific constellations of symptoms. In addition, this signature can be used as a biomarker to support early diagnoses and to potentially reveal pathological changes associated with said disorder. Here, we characterized the cortical atrophy signature of behavioural variant frontotemporal dementia (bvFTD). We used a data-driven approach to estimate cortical thickness using surface-based analyses in two independent, sporadic bvFTD samples (n = 30 and n = 71, total n = 101), using age- and gender-matched cognitively and behaviourally normal individuals. We found highly similar patterns of cortical atrophy across the two independent samples, supporting the reliability of our bvFTD signature. Next, we investigated whether our bvFTD signature targets specific large-scale cortical networks, as is the case for other neurodegenerative disorders. We specifically asked whether the bvFTD signature topographically overlaps with the salience network, as previous reports have suggested. We hypothesized that because phenotypic presentations of bvFTD are diverse, this would not be the case, and that the signature would cross canonical network boundaries. Consistent with our hypothesis, the bvFTD signature spanned rostral portions of multiple networks, including the default mode, limbic, frontoparietal control and salience networks. We then tested whether the signature comprised multiple anatomical subtypes, which themselves overlapped with specific networks. To explore this, we performed a hierarchical clustering analysis. This yielded three clusters, only one of which extensively overlapped with a canonical network (the limbic network). Taken together, these findings argue against the hypothesis that the salience network is preferentially affected in bvFTD, but rather suggest that-at least in patients who meet diagnostic criteria for the full-blown syndrome-neurodegeneration in bvFTD encompasses a distributed set of prefrontal, insular and anterior temporal nodes of multiple large-scale brain networks, in keeping with the phenotypic diversity of this disorder.