Salience Research Articles

Ventral Striatum is Preferentially Correlated with the Salience Network Including Regions in Dorsolateral Prefrontal Cortex.

The ventral striatum (VS) receives input from the cerebral cortex and is modulated by midbrain dopaminergic projections in support of processing reward and motivation. Here we explored the organization of cortical regions linked to the human VS using within-individual functional connectivity MRI in intensively scanned participants. In two initial participants (scanned 31 sessions each), seed regions in the VS were preferentially correlated with distributed cortical regions that are part of the Salience (SAL) network. The VS seed region recapitulated SAL network topography in each individual including anterior and posterior midline regions, anterior insula, and dorsolateral prefrontal cortex (DLPFC) - a topography that was distinct from a nearby striatal seed region. The region of DLPFC linked to the VS is positioned adjacent to regions associated with domain-flexible cognitive control. The full pattern was replicated in independent data from the same two individuals and generalized to 15 novel participants (scanned 8 or more sessions each). These results suggest that the VS forms a cortico-basal ganglia loop as part of the SAL network. The DLPFC is a neuromodulatory target to treat major depressive disorder. The present results raise the possibility that the DLPFC may be an effective neuromodulatory target because of its preferential coupling to the VS and suggests a path toward further personalization.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A multi-experiment iEEG replication.

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience (SN), default mode (DMN), and frontoparietal (FPN) networks, provides a framework for understanding these interactions. We analyzed intracranial EEG recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting-state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally-driven memory encoding and internally-governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

Evaluation of Intra- and Inter-Network Connectivity within Major Brain Networks in Drug-Resistant Depression Using rs-fMRI.

Background: Major Depressive Disorder (MDD) is a significant challenge in modern medicine due to its unclear underlying causes. Brain network dysfunction is believed to play a key role in its pathophysiology. Resting-state functional MRI (rs-fMRI), a neuroimaging technique, enables the in vivo assessment of functional connectivity (FC) between brain regions, offering insights into these network dysfunctions. The aim of this study was to evaluate abnormalities in FC within major brain networks in patients with drug-resistant MDD. Methods: The study group consisted of 26 patients with drug-resistant MDD and an age-matched control group (CG) of 26 healthy subjects. The rs-fMRI studies were performed on a 3T MR scanner (Philips, Ingenia) using a 32-channel head and neck coil. Imaging data were statistically analyzed, focusing on the intra- and inter-network FC of the following networks: default mode (DMN), sensorimotor (SMN), visual (VN), salience (SN), cerebellar (CN), dorsal attention (DAN), language (LN), and frontoparietal (FPN). Results: In patients with MDD, the intra-network analysis showed significantly decreased FC between nodes within VN compared to CG. In contrast, the inter-network analysis showed significantly increased FC between nodes from VN and SN or VN and DAN compared to CG. Decreased FC was found between SN and CN or SN and FPN as well as VN and DAN nodes compared to CG. Conclusions: Patients with MDD showed significant abnormalities in resting-state cortical activity, mainly regarding inter-network functional connectivity. These results contribute to the knowledge on the pathomechanism of MDD and may also be useful for developing new treatments.

Associations Between Changes in Psychological Resilience and Resting-State Functional Connectivity Throughout Pediatric Concussion Recovery.

Purpose: This study investigated the association between psychological resilience and resting-state network functional connectivity of three major brain networks in pediatric concussion. Methods: This was a substudy of a randomized controlled trial, recruiting children with concussion and orthopedic injury. Participants completed the Connor-Davidson Resilience 10 Scale and underwent magnetic resonance imaging at 72 h and 4-weeks postinjury. We explored associations between resilience and connectivity with the default mode network (DMN), central executive network (CEN), and salience network (SN) at both timepoints and also any change that occurred over time. We also explored associations between resilience and connectivity within each network. Results: A total of 67 children with a concussion (median age = 12.87 [IQR: 11.79-14.36]; 46% female) and 30 with orthopedic injury (median age = 12.27 [IQR: 11.19-13.94]; 40% female) were included. Seed-to-voxel analyses detected a positive correlation between 72-h resilience and CEN connectivity in the concussion group. Group moderated associations between resilience and SN connectivity at 72 h, as well as resilience and DMN connectivity over time. Regions-of-interest analyses identified group as a moderator of longitudinal resilience and within-DMN connectivity. Conclusions: These results suggest that neural recovery from concussion could be reliant on resilience. Resilience was related to functional connectivity with three of the main networks in the brain that are often impacted by concussion. Improving resilience might be investigated as a modifiable variable in children as both a protective and restorative in the context of concussion. Clinical Trial Registration Identifier: NCT05105802. PedCARE+MRI team (see Supplementary Appendix S1).

Multimodal 7T imaging reveals enhanced functional coupling between salience and frontoparietal networks in young adult tobacco cigarette smokers.

Tobacco cigarette smoking is associated with disrupted brain network dynamics in resting brain networks including the Salience (SN) and Fronto parietal (FPN). Unified multimodal methods [Resting state connectivity analysis, Diffusion Tensor Imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and cortical thickness analysis] were employed to test the hypothesis that the impact of cigarette smoking on the balance among these networks is due to alterations in white matter connectivity, microstructural architecture, functional connectivity and cortical thickness (CT) and that these metrics define fundamental differences between people who smoke and nonsmokers. Multimodal analyses of previously collected 7 Tesla MRI data via the Human Connectome Project were performed on 22 people who smoke (average number of daily cigarettes was 10 ± 5) and 22 age- and sex-matched nonsmoking controls. First, functional connectivity analysis was used to examine SN-FPN-DMN interactions between people who smoke and nonsmokers. The anatomy of these networks was then assessed using DTI and CT analyses while microstructural architecture of WM was analyzed using the NODDI toolbox. Seed-based connectivity analysis revealed significantly enhanced within network [p = 0.001 FDR corrected] and between network functional coupling of the salience and R-frontoparietal networks in people who smoke [p = 0.004 FDR corrected]. The network connectivity was lateralized to the right hemisphere. Whole brain diffusion analysis revealed no significant differences between people who smoke and nonsmokers in Fractional Anisotropy, Mean diffusivity and in neurite orienting and density. There were also no significant differences in CT in the hubs of these networks. Our results demonstrate that tobacco cigarette smoking is associated with enhanced functional connectivity, but anatomy is largely intact in young adults. Whether this enhanced connectivity is pre-existing, transient or permanent is not known. The observed enhanced connectivity in resting state networks may contribute to the maintenance of smoking frequency.

Developmental trajectories of the default mode, frontoparietal, and salience networks from the third trimester through the newborn period

Abstract The default mode (DMN), frontoparietal (FPN), and salience (SN) networks interact to support a range of behaviors, are vulnerable to environmental insults, and are disrupted in neurodevelopmental disorders. However, their development across the third trimester and perinatal transition remains unknown. Employing resting-state functional MRI at 30 to 32, 34 to 36, and 40 to 44 weeks postmenstrual age (PMA), we examined developmental trajectories of the intra- and internetwork connectivity of the 3 networks measured in 84 fetuses and neonates. A secondary analysis addressed the impact of maternal mental health on these networks. The DMN, FPN, and SN intranetwork connectivity evidenced significant increases between 36 and 44 weeks PMA, with connectivity measures reaching values significantly greater than 0 at 40 weeks PMA for all 3 networks. Connectivity between SN and DMN and between SN and FPN decreased significantly with the connectivity values significantly below 0 at 36–44 weeks. However, DMN-FPN connectivity increased between 30 and 44 weeks with the connectivity greater than 0 already at 36 months. Finally, higher maternal stress levels negatively affected the SN across 30-44 weeks PMA. These data provide a normative framework to compare fetuses and neonates at risk for neurobehavioral disorders and assess the impact of the environment on the developing brain.

Acute Exercise Improves Large-Scale Brain Network Segregation in Healthy Older Adults.

Introduction: Age-related cognitive decline and mental health problems are accompanied by changes in resting-state functional connectivity (rsFC) indices, such as reduced brain network segregation. Meanwhile, exercise can improve cognition, mood, and neural network function in older adults. Studies on effects of exercise on rsFC outcomes in older adults have chiefly focused on changes after exercise training and suggest improved network segregation through enhanced within-network connectivity. However, effects of acute exercise on rsFC measures of neural network integrity in older adults, which presumably underlie changes observed after exercise training, have received less attention. In this study, we hypothesized that acute exercise in older adults would improve functional segregation of major cognition and affect-related brain networks. Methods: To test this, we analyzed rsFC data from 37 healthy and physically active older adults after they completed 30 min of moderate-to-vigorous intensity cycling and after they completed a seated rest control condition. Conditions were performed in a counterbalanced order across separate days in a within-subject crossover design. We considered large-scale brain networks associated with cognition and affect, including the frontoparietal network (FPN), salience network (SAL), default mode network (DMN), and affect-reward network (ARN). Results: We observed that after acute exercise, there was greater segregation between SAL and DMN, as well as greater segregation between SAL and ARN. Conclusion: These findings indicate that acute exercise in active older adults alters rsFC measures in key cognition and affect-related networks in a manner that opposes age-related dedifferentiation of neural networks that may be detrimental to cognition and mental health.

Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment

Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment

Educational Attainment Moderates Task-State Control Network Connectivity Relations to Response Conflict Among Healthy Older Adults.

Older adult executive function varies widely due to brain and cognitive aging. Variance in older adult executive function is linked to increased response conflict from cognitive and brain aging. Cognitive reserve (CR) is a theoretical protective mechanism that lessens brain aging's impact on cognition and is associated with greater educational attainment. Recent work in rest-state functional magnetic resonance imaging (fMRI) suggests CR proxies moderate the relationship between functional connectivity (FC) and cognitive performance. Brain network FC in "control networks," including the salience (SN), dorsal attention and frontoparietal networks, are associated with cognitive processes in older adults. CR is hypothesized to maintain cognitive processing in part through changes in how brain networks respond to cognitive demands. However, it is unclear how CR proxies like educational attainment are related to control network FC during performance when cognitive demands are increased relative to rest. Because CR is expressed more in those with higher education, we hypothesized stronger control network FC would relate to better performance, where this relationship would be strongest among the most educated. We collected flanker task data during fMRI to assess the impact of a CR proxy (i.e., educational attainment) on response conflict among older adult subjects (n = 42, age = 65-80). Linear mixed-effects models showed more educated older adults with greater SN-FC had a smaller flanker effect (i.e., less influence of distractors; p < .001) during task performance. For the first time, we show that educational attainment moderates the relationship between task-state SN-FC and executive function among older adults.

Structural brain network degeneration associated with agitation in dementia

AbstractBackgroundAgitation is a behavioral syndrome involving increased motor activity, restlessness, aggressiveness and emotional distress. Its has a 30% prevalence across multiple types of dementia and is associated with negative outcomes, including reduced quality of life, caregiver distress, and mortality. Pharmacological treatment risks serious side effects, including mortality. Understanding brain network topology could provide insights into novel treatment methods.MethodParticipants comprised 600 subjects from 3 existing databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Frontotemporal Lobar Degeneration Neuroimaging Initiative (NIFD) and the Mesulam Center for Cognitive Neurology and Alzheimer’s Disease at Northwestern University. Participants were clinically diagnosed with either behavioral variant frontotemporal dementia (bvFTD), mild cognitive impairment (MCI), dementia of the Alzeheimer type (DAT) or cognitively normal (CN). The Neuropsychiatric Inventory Questionnaire (NPI‐Q) caregiver rating was used to determine whether individuals were agitated or not. Morphometric similarity networks (MSNs) were generated from Freesurfer statistics calculated from T1‐weighted MRIs. 7 surface‐based cortical metrics (e.g. gray matter volume, surface area) were calculated for each of 360 parcels. Pairwise inter‐parcel Pearson correlations of feature vectors were calculated to produce a morphometric similarity matrix for each individual. We calculated sub‐matrices for salience (SN) cognitive control (CCN) and default mode networks (DMN). Brain Connectivity Toolbox calculated transitivity and global efficiency of each network. Metrics were calculated at different thresholds to ensure the results were not threshold‐dependent. We calculated 2 (Agitation present/absent) x 4 (Diagnosis) repeated measures ANCOVAS for transitivity and global efficiency for each network. Covariates were age, sex, race, database, education, ICV, CDR‐SB and days MRI‐NPI‐Q.ResultFor the SN, people with Agitation had significantly lower global efficiency than people without Agitation (Figure 1). There were no significant effects of diagnosis or interaction. For the CCN, people with agitation had significantly lower transitivity than people without Agitation (Figure 2). There were no significant effect of diagnosis or interaction.ConclusionAcross different forms of dementia, Agitation is associated with reduced integration (global efficiency) of the salience network, and reduced segregation (transitivity) of the cognitive control network. Interventions that alter these topological network features may be effective in reducing agitation in dementia, regardless of clinical diagnosis.

Structural brain network degeneration associated with agitation in dementia

AbstractBackgroundAgitation is a behavioral syndrome involving increased motor activity, restlessness, aggressiveness and emotional distress. Its has a 30% prevalence across multiple types of dementia and is associated with negative outcomes, including reduced quality of life, caregiver distress, and mortality. Pharmacological treatment risks serious side effects, including mortality. Understanding brain network topology could provide insights into novel treatment methods.MethodParticipants comprised 600 subjects from 3 existing databases: the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Frontotemporal Lobar Degeneration Neuroimaging Initiative (NIFD) and the Mesulam Center for Cognitive Neurology and Alzheimer’s Disease at Northwestern University.Participants were clinically diagnosed with either behavioral variant frontotemporal dementia (bvFTD), mild cognitive impairment (MCI), dementia of the Alzeheimer type (DAT) or cognitively normal (CN). The Neuropsychiatric Inventory Questionnaire (NPI‐Q) caregiver rating was used to determine whether individuals were agitated or not.Morphometric similarity networks (MSNs) were generated from Freesurfer statistics calculated from T1‐weighted MRIs. 7 surface‐based cortical metrics (e.g. gray matter volume, surface area) were calculated for each of 360 parcels. Pairwise inter‐parcel Pearson correlations of feature vectors were calculated to produce a morphometric similarity matrix for each individual. We calculated sub‐matrices for salience (SN) cognitive control (CCN) and default mode networks (DMN). Brain Connectivity Toolbox calculated transitivity and global efficiency of each network. Metrics were calculated at different thresholds to ensure the results were not threshold‐dependent.We calculated 2 (Agitation present/absent) x 4 (Diagnosis) repeated measures ANCOVAS for transitivity and global efficiency for each network. Covariates were age, sex, race, database, education, ICV, CDR‐SB and days MRI‐NPI‐Q.ResultFor the SN, people with Agitation had significantly lower global efficiency than people without Agitation (Figure 1). There were no significant effects of diagnosis or interaction.For the CCN, people with agitation had significantly lower transitivity than people without Agitation (Figure 2). There were no significant effect of diagnosis or interaction.ConclusionAcross different forms of dementia, Agitation is associated with reduced integration (global efficiency) of the salience network, and reduced segregation (transitivity) of the cognitive control network. Interventions that alter these topological network features may be effective in reducing agitation in dementia, regardless of clinical diagnosis.

Differences between subclinical attention-deficit/hyperactivity and autistic traits in default mode, salience, and frontoparietal network connectivities in young adult Japanese

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are associated with attentional impairments, with both commonalities and differences in the nature of their attention deficits. This study aimed to investigate the neural correlates of ADHD and ASD traits in healthy individuals, focusing on the functional connectivity (FC) of attention-related large-scale brain networks (LSBNs). The participants were 61 healthy individuals (30 men; age, 21.9 ± 1.9 years). The Adult ADHD Self-Report Scale (ASRS) and Autism Spectrum Quotient (AQ) were administered as indicators of ADHD and ASD traits, respectively. Performance in the continuous performance test (CPT) was used as a behavioural measure of sustained attentional function. Functional magnetic resonance imaging scans were performed during the resting state (Rest) and auditory oddball task (Odd). Considering the critical role in attention processing, we focused our analyses on the default mode (DMN), frontoparietal (FPN), and salience (SN) networks. Region of interest (ROI)-to-ROI analyses (false discovery rate < 0.05) were performed to determine relationships between psychological measures with within-network FC (DMN, FPN, and SN) as well as with between-network FC (DMN-FPN, DMN-SN, and FPN-SN). ASRS scores, but not AQ scores, were correlated with less frequent commission errors and shorter reaction times in the CPT. During Odd, significant positive correlations with ASRS were demonstrated in multiple FCs within DMN, while significant positive correlations with AQ were demonstrated in multiple FCs within FPN. AQs were negatively correlated with FPN-SN FCs. During Rest, AQs were negatively and positively correlated with one FC within the SN and multiple FCs between the DMN and SN, respectively. These findings of the ROI-to-ROI analysis were only partially replicated in a split-half replication analysis, a replication analysis with open-access data sets, and a replication analysis with a structure-based atlas. The better CPT performance by individuals with subclinical ADHD traits suggests positive effects of these traits on sustained attention. Differential associations between LSBN FCs and ASD/ADHD traits corroborate the notion of differences in sustained and selective attention between clinical ADHD and ASD.

Resting-state functional connectivity changes in older adults with sleep disturbance and the role of amyloid burden.

Sleep and related disorders could lead to changes in various brain networks, but little is known about the role of amyloid β (Aβ) burden-a key Alzheimer's disease (AD) biomarker-in the relationship between sleep disturbance and altered resting state functional connectivity (rsFC) in older adults. This cross-sectional study examined the association between sleep disturbance, Aβ burden, and rsFC using a large-scale dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sample included 489 individuals (53.6% cognitively normal, 32.5% mild cognitive impairment, and 13.9% AD) who had completed sleep measures (Neuropsychiatric Inventory), PET Aβ data, and resting-state fMRI scans at baseline. Within and between rsFC of the Salience (SN), the Default Mode (DMN) and the Frontal Parietal network (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance was evaluated using the linear regressions, controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension. Although no significant main effect of sleep disturbance was found on rsFC, a significant interaction term emerged between sleep disturbance and Aβ burden on rsFC of SN (β = 0.11, P = 0.006). Specifically, sleep disturbance was associated with SN hyperconnectivity, only with the presence of Aβ burden. Sleep disturbance may lead to altered connectivity in the SN when Aβ is accumulated in the brain. Individuals with AD pathology may be at increased risk for sleep-related aberrant rsFC; therefore, identifying and treating sleep problems in these individuals may help prevent further disease progression.

Childhood trauma is linked to abnormal static-dynamic brain topology in adolescents with major depressive disorder

Childhood trauma is a leading risk factor for adolescents developing major depressive disorder (MDD); however, the underlying neuroimaging mechanisms remain unclear. This study aimed to investigate the association among childhood trauma, MDD and brain dysfunctions by combining static and dynamic brain network models. We recruited 46 first-episode drug-naïve adolescent MDD patients with childhood trauma (MDD-CT), 53 MDD patients without childhood trauma (MDD-nCT), and 90 healthy controls (HCs) for resting-state functional magnetic resonance imaging (fMRI) scans; all participants were aged 13–18 years. Compared to the HCs and MDD-nCT groups, the MDD-CT group exhibited significantly higher global and local efficiency in static brain networks and significantly higher temporal correlation coefficients in dynamic brain network models at the whole-brain level, and altered the local efficiency of default mode network (DMN) and temporal correlation coefficients of DMN, salience (SAN), and attention (ATN) networks at the local perspective. Correlation analysis indicated that altered brain network features and clinical symptoms, childhood trauma, and particularly emotional neglect were highly correlated in adolescents with MDD. This study may provide new evidence for the dysconnectivity hypothesis regarding the associations between childhood trauma and MDD in adolescents from the perspectives of both static and dynamic brain topology.

Dynamic attentional mechanisms of creative cognition

In popular imagination creativity requires us to surrender control. Yet, attention is at the heart of control, and many studies show attention to play a key role in the creative process. This is partly due to the selective nature of attention—creative cognition consists of two phases, idea generation and idea evaluation, and selective processes are essential for both phases. Here, we investigate attentional (i.e., selective) mechanisms underlying each phase, using the framework of two major attention taxonomies: top-down/bottom-up and internal/external attention. We argue that creative cognition is supported by a dynamic interplay between the typically opposing sides of each taxonomy. Further, we argue that this dynamic relationship is reflected in interactions across three large-scale brain networks: the default mode (DMN), frontoparietal control (FPN), and salience (SN) networks. Our review of the evidence suggests that creative cognition is best achieved through the flexible use of multiple forms of attention, rather than through reduced attention. We thus propose a two-dimensional space, including one dimension for top-down/bottom-up attention and another for internal/external attention, which can sufficiently capture the flexibility and diversity of attentional mechanisms underlying different stages and components of creative cognition.

Abnormal resting-state effective connectivity in large-scale networks among obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is a chronic mental illness characterized by abnormal functional connectivity among distributed brain regions. Previous studies have primarily focused on undirected functional connectivity and rarely reported from network perspective. To better understand between or within-network connectivities of OCD, effective connectivity (EC) of a large-scale network is assessed by spectral dynamic causal modeling with eight key regions of interests from default mode (DMN), salience (SN), frontoparietal (FPN) and cerebellum networks, based on large sample size including 100 OCD patients and 120 healthy controls (HCs). Parametric empirical Bayes (PEB) framework was used to identify the difference between the two groups. We further analyzed the relationship between connections and Yale-Brown Obsessive Compulsive Scale (Y-BOCS). OCD and HCs shared some similarities of inter- and intra-network patterns in the resting state. Relative to HCs, patients showed increased ECs from left anterior insula (LAI) to medial prefrontal cortex, right anterior insula (RAI) to left dorsolateral prefrontal cortex (L-DLPFC), right dorsolateral prefrontal cortex (R-DLPFC) to cerebellum anterior lobe (CA), CA to posterior cingulate cortex (PCC) and to anterior cingulate cortex (ACC). Moreover, weaker from LAI to L-DLPFC, RAI to ACC, and the self-connection of R-DLPFC. Connections from ACC to CA and from L-DLPFC to PCC were positively correlated with compulsion and obsession scores (r = 0.209, p = 0.037; r = 0.199, p = 0.047, uncorrected). Our study revealed dysregulation among DMN, SN, FPN, and cerebellum in OCD, emphasizing the role of these four networks in achieving top-down control for goal-directed behavior. There existed a top-down disruption among these networks, constituting the pathophysiological and clinical basis.

Resting‐State Connectivity Changes in Older Adults with Sleep Disturbance

AbstractBackgroundSleep and related disorders could lead to changes in various brain network connectivities, but little is known about the role of Aβ burden—a key Alzheimer’s disease (AD) biomarker—in the relationship between sleep disturbance and altered functional connectivity in older adults. This study examined the association between sleep disturbance, Aβ positivity, and with‐in and between resting‐state functional connectivity networks using a large‐scale dataset from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).MethodSample was consisted of 413 individuals (mean age 76.56±7.20; 57.5% cognitively normal, 30.3% mild cognitive impairment, and 12.1% AD) who had completed sleep measures (Neuropsychiatric Inventory), CSF‐Aβ42 data, and resting‐state fMRI scans at baseline. Functional connectivity within Salience (SN), Default Mode (DMN) and Frontal Parietal networks (FPN) were compared between participants with sleep disturbance versus without sleep disturbance. The interaction between Aβ positivity and sleep disturbance were evaluated controlling for age, diagnosis status, gender, sedatives and hypnotics use, and hypertension.ResultSleep disturbance was associated with reduced connectivity in the SN (β = 0.04, P = 0.006) but not with DMN or FPN. Additionally, sleep disturbance interacted significantly with Aβ positivity in altered with‐in connectivity of SN (β = 0.10, P = 0.003), DMN (β = 0.07, P = 0.0048), and FPN (β = 0.09, P = 0.03) networks.ConclusionSleep disturbance may lead to altered connectivity in the SN and further change various within and between functional networks in the presence of amyloid burden.

Effects of ambient fine particulates, nitrogen dioxide, and ozone on maturation of functional brain networks across early adolescence

BackgroundAir pollution is linked to neurodevelopmental delays, but its association with longitudinal changes in brain network development has yet to be investigated. We aimed to characterize the effect of PM2.5, O3, and NO2 exposure at ages 9–10 years on changes in functional connectivity (FC) over a 2-year follow-up period, with a focus on the salience (SN), frontoparietal (FPN), and default-mode (DMN) brain networks as well as the amygdala and hippocampus given their importance in emotional and cognitive functioning. MethodsA sample of children (N = 9,497; with 1–2 scans each for a total of 13,824 scans; 45.6% with two brain scans) from the Adolescent Brain Cognitive Development (ABCD) Study® were included. Annual averages of pollutant concentrations were assigned to the child’s primary residential address using an ensemble-based exposure modeling approach. Resting-state functional MRI was collected on 3T MRI scanners. First, developmental linear mixed-effect models were performed to characterize typical FC development within our sample. Next, single- and multi-pollutant linear mixed-effect models were constructed to examine the association between exposure and intra-network, inter-network, and subcortical-to-network FC change over time, adjusting for sex, race/ethnicity, income, parental education, handedness, scanner type, and motion. ResultsDevelopmental profiles of FC over the 2-year follow-up included intra-network integration within the DMN and FPN as well as inter-network integration between the SN-FPN; along with intra-network segregation in the SN as well as subcortical-to-network segregation more broadly. Higher PM2.5 exposure resulted in greater inter-network and subcortical-to-network FC over time. In contrast, higher O3 concentrations resulted in greater intra-network, but less subcortical-to-network FC over time. Lastly, higher NO2 exposure led to less inter-network and subcortical-to-network FC over the 2-year follow-up period. ConclusionTaken together, PM2.5, O3, and NO2 exposure in childhood relate to distinct changes in patterns of network maturation over time. This is the first study to show outdoor ambient air pollution during childhood is linked to longitudinal changes in brain network connectivity development.

Reconfigured metabolism brain network in asymptomatic microtubule‐associated protein tau mutation carriers: A graph‐theoretical analysis

AbstractBackgroundStudies exploring topologic properties of the metabolic network in the presymptomatic stage of genetic frontotemporal dementia (FTD) are scarce, which may be important for understanding brain function and disease pathogenesis. This study aimed to explore FTD‐specific patterns of metabolism topology reconfiguration in asymptomatic microtubule‐associated protein tau (MAPT) mutation carriers.MethodSix asymptomatic carriers of MAPT P301L mutation were compared with 12 non‐carriers who were all from the same large family of FTD. For comparison, we included 32 behavioral‐variant FTD (bvFTD) patients and 33 unrelated healthy controls. Each participant underwent neuropsychological assessments, genetic testing, and hybrid PET/MRI scan. Voxel‐wise gray matter volume and standardized uptake value ratio were calculated and compared for structural MRI and FDG‐PET, separately. The sparse inverse covariance estimation (SICE) method was applied to topologic properties and metabolic connectomes of brain functional networks derived from 18F‐fluorodeoxyglucose PET/MRI data. Independent component analysis was used to explore metabolic connectivity in the salience (SN) and default mode networks (DMN).ResultThe mean estimated years from symptom onset was 8.33 ± 1.875 years in the mutation carrier group. The asymptomatic MAPT carriers show no significant differences in cognitive tests, gray matter volume and FDG uptake compared to non‐carriers. They also retained normal global parameters of metabolism network, but not for bvFTD patients. However, we revealed lost hubs in the ventromedial prefrontal, orbitofrontal, and anterior cingulate cortices and reconfigured hubs in the anterior insula, precuneus, and posterior cingulate cortex in asymptomatic carriers, which is consistent with the findings in bvFTD patients. Similarly, significant differences in local parameters of these nodes were observed in asymptomatic carriers. The reduction in connectivity was marked in lost hub regions during the asymptomatic stage. Functional connectivity within the SN and DMN was enhanced in asymptomatic carriers, while reduced in bvFTD patients.ConclusionOur findings showed that asymptomatic MAPT carriers were initially involved in medial prefrontal areas and actively compensated in task‐related regions. Topologic properties of the metabolic network may contribute to further investigations and monitoring of the earliest stages of FTD in individuals with genetic backgrounds.

0196 The relationship between brain functional connectivity and objective and subjective assessments of chronic habitual sleep status

Abstract Introduction Sleep is a multiscale phenomenon. It has subjective and objective components often not highly correlated. Given the importance of brain networks for cognition and evidence that sleep deprivation and variability in habitual sleep patterns affects Functional brain Connectivity (FC), we would expect brain networks would be differentially related to subjective and objective markers of sleep. As well as region specific studies, investigating the effect of habitual sleep measures across whole network interactions is important. This study investigates whether overall FC from 3 Intrinsically Connected Networks (ICN); Default mode(DMN), Salience(SN) and Central executive(CEN) network is differentially related to subjective and objective sleep measures from sleep quality questionnaire, sleep diary data and actigraphy metrics. Methods Functional Magnetic Resonance Imaging data acquired from 32 healthy adults (right handed, 14 female, age 23-59 years). Regions of interest (ROI) representing nodes of the ICN created from separate data of 55 subjects (28 male, age 25±4yrs). Seed-based FC analysis performed using in-house MATLAB code. Average FC across all paired connections in each ICN calculated to define overall mean FC. Two-way multiple measures factorial ANOVA split-plot analysis performed with a 2x3x6 design to determine any interaction of 6 sleep measures (Pittsburgh sleep quality index, subjective sleep efficiency, diary total sleep time, wake after sleep onset, total sleep time, sleep efficiency) with the FC of the ICN. Pairwise comparisons for significant interactions and univariate analysis on the linearly independent pairwise comparisons among the estimated marginal means performed to confirm significant interactions between ICN FC and sleep measures. Results No significant interactions for any of the sleep metrics tested, subjective or quantitative (P=&amp;gt;0.05). Except subjective sleep efficiency (SSE) which demonstrated a significant within subject interaction of overall network FC (F(2,30) = 3.682, P = 0.033, n2=0.109) for CEN. Conclusion Significant findings of SSE in relation to CEN demonstrate increased FC of the CEN in relation to higher SSE and lower CEN FC in subjects with low SSE. Overall findings suggests FC changes within ICN in relation to chronic habitual sleep status metrics (subjective or quantitative) don’t involve whole network FC, therefore we believe them to be region specific. Support (if any)