Connectivity Patterns Research Articles

Functional Neuroanatomy of the Normal and Pathological Basal Ganglia.

The term "basal ganglia" refers to a group of interconnected subcortical nuclei engaged in motor planning and movement initiation, executive functions, behaviors, and emotions. Dopamine released from the substantia nigra is the underlying driving force keeping the basal ganglia network under proper equilibrium and, indeed, reduction of dopamine levels triggers basal ganglia dysfunction, setting the groundwork for several movement disorders. The canonical basal ganglia model has been instrumental for most of our current understanding of the normal and pathological functioning of this subcortical network. This model explains how cortical information flows through the basal ganglia nuclei back to the cortex by going through two pathways with opposing effects that together lead to the proper execution of a given movement. The basal ganglia model has paved the way for the standard clinical management of Parkinson's disease, where pharmacological and neurosurgical treatments in place collectively afford an impressive symptomatic alleviation. Although much of the model has remained, the canonical model has been enriched with new arrivals gathered from evidence provided in the last three decades. Here, we sought to provide a comprehensive review of the basal ganglia network, with emphasis on structure, connectivity patterns, and basic operational principles, both in normal and pathological conditions.

Altered dynamic functional connectivity in antagonistic state in first-episode, drug-naïve patients with major depressive disorder

ObjectiveMajor depressive disorder (MDD) is known to be characterized by disrupted brain functional network connectivity (FNC) patterns, while the dynamic change mode of different functional networks is unclear. This study aimed to characterize specific dynamic alterations pattern on intrinsic FNC in MDD by combining static FNC (sFNC) and dynamic FNC (dFNC).MethodsA total of 48 first-episode drug-naïve MDD and 48 matched healthy controls (HCs) were included in this study. The sFNC and dFNC were analyzed using complete time-series and sliding window approach, respectively. Both sFNC and dFNC differences between groups were analyzed and associations between disease severity and aberrant FNC were explored.ResultsMDD patients exhibited lower sFNC within and between sensory and motor networks than HC. Four dFNC states were identified, including a globally-weakly-connected state, a cognitive-control-dominated state, a globally-positively-connected state, and an antagonistic state. The antagonistic state was marked by strong positive connections within the sensorimotor domain and their anti-correlations with the executive-motor control domain. Notably, MDD patients exhibited significantly longer dwell time in the globally-weakly-connected state, at the cost of significantly shorter dwell time in the antagonistic state. Further, only the mean dwell time of this antagonistic state was significantly anticorrelated to disease severity measures.ConclusionsOur study highlights the altered dynamics of the antagonistic state as a fundamental aspect of disrupted FNC in early MDD.

How Connectivity in Foreign Direct Investment Networks Affects MNE Decision-Making and Performance

The foreign direct investment (FDI) activity of multinational enterprises (MNEs) is inherently a multilateral phenomenon. In the real world, locations rarely receive investments from a single MNE, and MNEs seldom invest in one location. Thus, FDIs create links between locations (e.g., home-host) and connect MNEs with other collocated firms. My dissertation provides insights into how the structural properties of the locations and MNEs, i.e., patterns of relationships and connectivity, influence the decisions and observed behaviors of MNEs. By analyzing MNEs’ interactions in different networks, my dissertation assesses the varying influence of different networks on MNEs’ strategies.

Distinct Neural Connectivity Patterns During Music Listening and Imagination: An Electroencephalography Study.

Background: The brain's function changes during various activities, and numerous studies have explored this field. An intriguing and significant area of research is the brain's functioning during imagination and periods of inactivity. Objective: This study explores the differences in brain connectivity during music listening and imagination: by identifying distinct neural connectivity patterns and providing insights into the cognitive mechanisms underlying auditory imagination. Methods: Effective connectivity matrices were generated using generalized partial directed coherence (GPDC) and directed Directed Transfer Function (dDTF) methods applied to non-invasive electroencephalography data from these two conditions. Statistical tests were performed to illustrate the differences in brain connectivity, followed by the creation of brain graphs and the application of a non-parametric permutation test to demonstrate statistical significance. Data classification between listening to music and imagining it was performed using an Support Vector Machine (SVM) classifier with different feature vectors. Results: Combining features extracted from GPDC and dDTF achieved an accuracy of 71.3% while using GPDC and dDTF features individually yielded accuracies of 60% and 62.1%, respectively. Among all the graph's global features, only modularity and small-worldness showed statistically significant differences in dDTF and GPDC. Overall, findings reveal that information flows from the left hemisphere to the right hemisphere increases during music imagination compared with listening, highlighting distinct neural connectivity patterns associated with imaginative processes. Conclusion: The study provides novel insights into the distinct neural connectivity patterns during music listening and imagination, contributing to the broader understanding of cognitive processes associated with auditory imagination and perception.

Identifying neural network structures explained by personality traits: combining unsupervised and supervised machine learning techniques in translational validity assessment

AbstractThere have been studies previously the neurobiological underpinnings of personality traits in various paradigms such as psychobiological theory and Eysenck’s model as well as five-factor model. However, there are limited results in terms of co-clustering of the functional connectivity as measured by functional MRI, and personality profiles. In the present study, we have analyzed resting-state connectivity networks and character type with the Lowen bioenergetic test in 66 healthy subjects. There have been identified direct correspondences between network metrics such as eigenvector centrality (EC), clustering coefficient (CC), node strength (NS) and specific personality characteristics. Specifically, N Acc L and OFCmed were associated with oral and masochistic traits in terms of EC and CC, while Insula R is associated with oral traits in terms of NS and EC. It is noteworthy that we observed significant correlations between individual items and node measures in specific regions, suggesting a more targeted relationship. However, the more relevant finding is the correlation between metrics (NS, CC, and EC) and overall traits. A hierarchical clustering algorithm (agglomerative clustering, an unsupervised machine learning technique) and principal component analysis were applied, where we identified three prominent principal components that cumulatively explain 76% of the psychometric data. Furthermore, we managed to cluster the network metrics (by unsupervised clustering) to explore whether neural connectivity patterns could be grouped based on combined average network metrics and psychometric data (global and local efficiencies, node strength, eigenvector centrality, and node strength). We identified three principal components, where the cumulative amount of explained data reaches 99%. The correspondence between network measures (CC and NS) and predictors (responses to Lowen’s items) is 62% predicted with a precision of 90%.

Differences in Network Functional Connectivity in Response to Sub-Symptomatic Exercise Between Elite Adult Athletes after Sport-Related Concussion and Healthy Matched Controls: A Pilot Study.

Resting-state electroencephalography (rsEEG) has developed as a method to explore functional network alterations related to sport-related concussion (SRC). Although exercise is an integral part of an athlete's return to sport (RTS) protocol, our understanding of the effects of exercise on (impaired) brain network activity in elite adult athletes is limited. However, this information may be beneficial to inform recovery and RTS progressions. Recording (128-channel) rsEEG datasets before and after a standardized moderate aerobic bike exercise test, this study aimed to explore functional connectivity patterns in whole brain and relevant functional networks in a group of elite adult athletes post-injury compared with healthy matched controls. The following networks were selected a priori: whole brain (68 regions of interest [ROIs]), default mode network (14 ROIs), central autonomic network (CAN, 24 ROIs), and visual network (8 ROIs). Twenty-one SRC athletes and 21 age-, sex-, sport type-, and skill level-matched healthy controls participated in this study. The SRC athletes were recruited during their RTS protocol (days since injury: 2-140 days). All athletes were able to achieve the exercise goal of reaching a moderate intensity (70% of their age-calculated maximum heart rate) while staying sub-symptomatic. Before and after exercise, functional connectivity was calculated by the phase locking value, in the alpha band (7-13 Hz). Mann-Whitney U and Wilcoxon signed rank tests were used to explore neurophysiological differences between and within groups, respectively. Whole-brain connectivity increased significantly from pre- to post-exercise within both groups (SRC: 0.264-0.284; p = 0.011 vs. controls: 0.253-0.257; p = 0.011). While CAN connectivity significantly increased only within the SRC group from pre-(0.298) to post-exercise (0.317; p = 0.003). Although all athletes reached their exercise goal without exacerbation of symptoms, the impact of exercise on the CAN appears to be greater for the SRC athletes, than matched healthy controls. The potential clinical significance of this finding is that it may have revealed an underlying mechanism for the cardiac autonomic alterations post-injury. This study merits further investigation into the CAN, as a network of interest more closely aligned with the clinical features (e.g., autonomic dysfunction) during athletes' RTS.

Shakespearean Spiders and Shakespearean Slime Mold

This article draws on the debts to Shakespeare—in particular Hamlet , Macbeth , The Merchant of Venice and The Tempest —in Adrian Tchaikovsky’s Children of Time (2015–22) novels to illuminate the trilogy’s complex engagement with posthuman identities. It identifies and analyzes a sustained pattern of connections between Tchaikovsky’s allusive processes and the various biological mechanisms of transformation and inheritance which drive the trilogy’s complex plot. The Shakespearean subtext becomes particularly resonant at moments when human survival or identity is challenged by unfamiliar life forms. In response to critical disagreement over whether Tchaikovsky embraces or rejects an anthropocentric view of the universe, it argues that Shakespeare’s own practice of presenting ethical or political debates in utramque partem , on both sides of the question, provides a model for decoding Tchaikovsky’s complex, shifting, and playful exploration of humanity’s encounters with very different life forms.

Unraveling migratory corridors of loggerhead and green turtles from the Yucatán Peninsula and its overlap with bycatch zones of the Northwest Atlantic.

Bycatch represents a conservation problem when endangered species are affected. Sea turtles are highly vulnerable to this threat as their critical habitats overlap with fishing zones in all regions of the world. We used sequences of the mitochondrial DNA control region obtained from loggerhead (Caretta caretta) and green (Chelonia mydas) turtles to determine the migratory routes between nesting habitats in the Yucatán Peninsula and their critical marine habitats in the Northwest Atlantic. Mixed Stock Analysis revealed that loggerheads from Quintana Roo migrated to foraging areas in the northwestern Atlantic. Migratory routes used by green turtles are determined by their natal nesting colony: (1) green turtles from the Gulf of Mexico migrate to foraging aggregations in Texas and the northern Gulf of Mexico, (2) Mexican Caribbean turtles travel to foraging grounds in Florida, and (3) a smaller proportion of individuals born in the Yucatán Peninsula display a local connectivity pattern. Our results suggest that the migratory corridors used by Mexican loggerheads overlap with longline fisheries in the mid-Atlantic where sea turtle bycatch is comprised predominantly of immature individuals. Green turtles from the Yucatán Peninsula migrate to critical habitats that overlap with shrimp trawl fisheries within the Gulf of Mexico. Bycatch data and the identification of migratory corridors used by loggerheads and green turtles suggests that shrimp trawl fisheries on the east coast of the U.S. and the Gulf of Mexico pose a serious threat to the conservation and recovery of Mexican sea turtle populations.

Cortico-cortical connectivity of the somatosensory cortex of the agouti: Topographical organization and evolutionary implications.

Understanding patterns of cortico-cortical connections in both frequently and infrequently studied species advances our knowledge of cortical organization and evolution. The agouti (Dasyprocta aguti, a medium-size South American rodent) offers a unique opportunity, because of its large lissencephalic brain and its natural behaviors, such as gnawing and hiding seeds, that require bimanual interaction while sitting on its hindlimbs and aligning its head to receive images of the horizon on the retinal visual streak. There have been no previous studies of the intrinsic and extrinsic ipsilateral projections of the agouti's primary somatosensory cortical area (S1). In the present study, we utilized biotinylated dextran (BDA) anatomical tract-tracer injections combined with microelectrode electrophysiological mapping, correlated with analysis of cytochrome oxidase (CO) histochemical staining, to investigate the ipsilateral corticocortical connectivity of the agouti's S1. By injecting BDA into electrophysiologically identified regions within the S1, we revealed ipsilateral intrinsic connections, as well as connections with cortical areas rostral and caudal to S1, and homotopic labeling in the second somatosensory cortical area (S2). In addition, we identified a focal cluster of labeled axons and axonal terminals adjacent to the rhinal fissure, tentatively named the parietal rhinal area (PR). The analysis of CO reactivity allowed delineation of the boundaries and subdivisions of S1, as well as the locations and limits of primary auditory and visual areas. These findings provide support for the notion of a similar pattern of somatosensory cortical organization and connectivity across mammalian species.

Whole brain functional connectivity: Insights from next generation neural mass modelling incorporating electrical synapses.

The ready availability of brain connectome data has both inspired and facilitated the modelling of whole brain activity using networks of phenomenological neural mass models that can incorporate both interaction strength and tract length between brain regions. Recently, a new class of neural mass model has been developed from an exact mean field reduction of a network of spiking cortical cell models with a biophysically realistic model of the chemical synapse. Moreover, this new population dynamics model can naturally incorporate electrical synapses. Here we demonstrate the ability of this new modelling framework, when combined with data from the Human Connectome Project, to generate patterns of functional connectivity (FC) of the type observed in both magnetoencephalography and functional magnetic resonance neuroimaging. Some limited explanatory power is obtained via an eigenmode description of frequency-specific FC patterns, obtained via a linear stability analysis of the network steady state in the neigbourhood of a Hopf bifurcation. However, direct numerical simulations show that empirical data is more faithfully recapitulated in the nonlinear regime, and exposes a key role of gap junction coupling strength in generating empirically-observed neural activity, and associated FC patterns and their evolution. Thereby, we emphasise the importance of maintaining known links with biological reality when developing multi-scale models of brain dynamics. As a tool for the study of dynamic whole brain models of the type presented here we further provide a suite of C++ codes for the efficient, and user friendly, simulation of neural mass networks with multiple delayed interactions.

Positive association between dietary total sugar intake and eosinophil counts in asthmatic children and adolescents in the United States: a cross-sectional study

s Background There is not enough data currently available to determine a correlation between eosinophil count and total sugar intake in the diet. Objective To clarify the relationship between eosinophil levels and overall sugar intake in the diet. Methods A cross-sectional study that involved 2013 people used data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2020. The relationship between total sugar intake and eosinophil count was investigated using multiple linear regression models. The linear relationship between the two was tested using a restricted cubic spline. Stratified analysis further confirms the stability of the findings. Results With a mean age of 11.6 ± 4.8 years and 56.5% male participation, all 2013 eligible participants were included. Fully adjusted model indicated a positive correlation between total dietary sugar intake and eosinophil counts (β = 0.41; 95% CI:0.15 to 0.68; P = 0.002), as determined by multivariable linear regression adjusted for risk variables. A multiple regression model using three quartiles of the total amount of sugar consumed did not change the significance of this outcome. Moreover, by limiting the triple spline, a linear relationship (p = 0.089) was discovered between the overall amount of sugar consumed and the eosinophil count. Subgroup analysis revealed an identical pattern of connection (p values for all interactions were larger than 0.05). Conclusion In U.S. children and adolescents with asthma, total dietary sugar intake was positively correlated with eosinophil levels. The findings are significant.

Sub-acute stroke demonstrates altered beta oscillation and connectivity pattern in working memory

IntroductionWorking memory (WM) is suggested to play a pivotal role in relearning and neural restoration during stroke rehabilitation. Using EEG, this study investigated the oscillatory mechanisms of WM in subacute stroke.MethodsThis study included 48 first subacute stroke patients (26 good-recovery, 22 poor-recovery, based on prognosis after a 4-week period) and 24 matched health controls. We examined the oscillatory characteristics and functional connectivity of the 0-back WM paradigm and assessed their associations with prognosis.ResultsPatients of poor recovery are characterised by a loss of significant beta rebound, beta-band connectivity, as well as impaired working memory speed and performances. Meanwhile, patients with good recovery have preserved these capacities to some extent. Our data further identified beta rebound to be closely associated with working memory speed and performances.ConclusionsWe provided novel findings that beta rebound and network connectivity as mechanistic evidence of impaired working memory in subacute stroke. These oscillatory features could potentially serve as a biomarker for brain stimulation technologies in stroke recovery.

Functional Connectivity Biomarker Extraction for Schizophrenia Based on Energy Landscape Machine Learning Techniques.

Brain connectivity represents the functional organization of the brain, which is an important indicator for evaluating neuropsychiatric disorders and treatment effects. Schizophrenia is associated with impaired functional connectivity but characterizing the complex abnormality patterns has been challenging. In this work, we used resting-state functional magnetic resonance imaging (fMRI) data to measure functional connectivity between 55 schizophrenia patients and 63 healthy controls across 246 regions of interest (ROIs) and extracted the disease-related connectivity patterns using energy landscape (EL) analysis. EL analysis captures the complexity of brain function in schizophrenia by focusing on functional brain state stability and region-specific dynamics. Age, sex, and smoker demographics between patients and controls were not significantly different. However, significant patient and control differences were found for the brief psychiatric rating scale (BPRS), auditory perceptual trait and state (APTS), visual perceptual trait and state (VPTS), working memory score, and processing speed score. We found that the brains of individuals with schizophrenia have abnormal energy landscape patterns between the right and left rostral lingual gyrus, and between the left lateral and orbital area in 12/47 regions. The results demonstrate the potential of the proposed imaging analysis workflow to identify potential connectivity biomarkers by indexing specific clinical features in schizophrenia patients.

NetQDA: Local Network-Guided High-Dimensioanl Quadratic Discriminant Analysis

Quadratic Discriminant Analysis (QDA) is a well-known and flexible classification method that considers differences between groups based on both mean and covariance structures. However, the connection structures of high-dimensional predictors are usually not explicitly incorporated into modeling. In this work, we propose a local network-guided QDA method that integrates the local connection structures of high-dimensional predictors. In the context of gene expression research, our method can identify genes that show differential expression levels as well as gene networks that exhibit different connection patterns between various biological state groups, thereby enhancing our understanding of underlying biological mechanisms. Extensive simulations and real data applications demonstrate its superior performance in both feature selection and outcome classification compared to commonly used discriminant analysis methods.

Are there cortical somatotopic motor maps outside of the human precentral gyrus?

Human body movements are supported by a somatotopic map - primary motor cortex (M1) - that is found along the precentral gyrus. Recent evidence has suggested two further motor maps that span the lateral occipitotemporal cortex (LOTC) and the precuneus. Confirmation of these maps is important, as they influence our understanding of the organisation of motor behaviour, for example by revealing how visual- and motor-related activity interact. However, evidence for these recently proposed maps is limited. We analysed an open fMRI dataset of 62 participants who moved performed twelve different body part movements. We analysed the magnitude of responses evoked by movements with novel quantitative indices that test for map-like organisation. We found strong evidence for bilateral somatotopic maps in precentral and postcentral gyri. In LOTC, we found much weaker responses to movement, and little evidence of somatotopy. In the precuneus, we found only limited evidence for somatotopy. We also adopted a background connectivity approach to examine correlations between M1, LOTC and the precuneus in the residual time-series data. This revealed a ventral-posterior / dorsal-anterior distinction in the connectivity between precuneus and M1, favouring the head and arms, respectively. Posterior right hemisphere LOTC showed some evidence of preferential connectivity to arm-selective regions of M1. Overall, our results do not support the existence of a somatotopic motor map in LOTC but provide some support for a coarse map in the precuneus, especially as revealed in connectivity patterns. These findings help clarify the organisation of human motor representations beyond the precentral gyrus.

Individualized mouse brain network models produce asymmetric patterns of functional connectivity after simulated traumatic injury

Abstract The functional and cognitive effects of traumatic brain injury (TBI) are poorly understood, as even mild injuries (concussion) can lead to long-lasting, untreatable symptoms. Simplified brain dynamics models may help researchers better understand the relationship between brain injury patterns and functional outcomes. Properly developed, these computational models provide an approach to investigate the effects of both computational and in vivo injury on simulated dynamics and cognitive function, respectively, for model organisms. In this study, we apply the Kuramoto model and an existing mesoscale mouse brain structural network to develop a simplified computational model of mouse brain dynamics. We explore how to optimize our initial model to predict existing mouse brain functional connectivity collected from mice under various anesthetic protocols. Finally, to determine how strongly the changes in our optimized models’ dynamics can predict the extent of a brain injury, we investigate how our simulations respond to varying levels of structural network damage. Results predict a mixture of hypo- and hyperconnectivity after experimental TBI, similar to results in TBI survivors, and also suggest a compensatory remodeling of connections which may have an impact on functional outcome after TBI.

Combined topological and spatial constraints are required to capture the structure of neural connectomes

Abstract Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints—either given by pairwise distances or the contactome—play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. Yet, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent mismatch by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics.

Investigating Task-Free Functional Connectivity Patterns in Newborns Using Functional Near-Infrared Spectroscopy.

Resting-state networks (RSNs), particularly the sensorimotor network, begin to strengthe in the third trimester of pregnancy and mature extensively by term age. The integrity and structure of these networks have been repeatedly linked to neurological health outcomes in neonates, highlighting the importance of understanding the normative variations in RSNs in healthy development. Specifically, robust bilateral functional connectivity in the sensorimotor RSN has been linked to optimal neurodevelopmental outcomes in neonates. In the current study, we aimed to map the developmental trajectory of the sensorimotor RSN in awake neonates using functional near-infrared spectroscopy (fNIRS). We acquired fNIRS resting-state data from 41 healthy newborns (17 females, gestational age ranging from 36+0 to 42+1weeks) within the first week after birth. We performed both single channel and hemispheric analyses to investigate the relationship between functional connectivity and both gestational and postnatal age. We observed robust positive connectivity in numerous channel-pairs across the sensorimotor network, especially in the left hemisphere. Next, we examined the relationship between functional connectivity, gestational age, and postnatal age, while controlling for sex and subject effects. We found both gestational and postnatal age to be significantly associated with changes in functional connectivity in the sensorimotor RSN. In our hemispheric analysis (Ninterhemispheric=10, Nleft intrahemispheric=15, and Nright intrahemispheric=9), we observed a significant positive relationship between interhemispheric connectivity and postnatal age. In summary, our findings demonstrate the utility of fNIRS for monitoring early developmental changes in functional networks in awake newborns.

Wasserstein Discriminant Dictionary Learning for Graph Representation.

Mining discriminative graph topological information plays an important role in promoting graph representation ability. However, it suffers from two main issues: (1) the difficulty/complexity of computing global inter-class/intra-class scatters, commonly related to mean and covariance of graph samples, for discriminant learning; (2) the huge complexity and variety of graph topological structure that is rather challenging to robustly characterize. In this paper, we propose the Wasserstein Discriminant Dictionary Learning (WDDL) framework to achieve discriminant learning on graphs with robust graph topology modeling, and hence facilitate graph-based pattern analysis tasks. Considering the difficulty of calculating global inter-class/intra-class scatters, a reference set of graphs (aka graph dictionary) is first constructed by generating representative graph samples (aka graph keys) with expressive topological structure. Then, a Wasserstein Graph Representation (WGR) process is proposed to project input graphs into a succinct dictionary space through the graph dictionary lookup. To further achieve discriminant graph learning, a Wasserstein discriminant loss (WD-loss) is defined on the graph dictionary, in which the graph keys are optimizable, to make the intra-class keys more compact and inter-class keys more dispersed. Hence, the calculation of global Wasserstein metric (W-metric) centers can be bypassed. For sophisticated topology mining in the WGR process, a joint-Wasserstein graph embedding module is constructed to model both between-node and between-edge relationships across inputs and graph keys by encapsulating both the Wasserstein metric (between cross-graph nodes) and proposed novel Kron-Gromov-Wasserstein (KGW) metric (between cross-graph adjacencies). Specifically, the KGW-metric comprehensively characterizes the cross-graph connection patterns with the Kronecker operation, then adaptively captures those salient patterns through connection pooling. To evaluate the proposed framework, we study two graph-based pattern analysis problems, i.e. graph classification and cross-modal retrieval, with the graph dictionary flexibly adjusted to cater to these two tasks. Extensive experiments are conducted to comprehensively compare with existing advanced methods, as well as dissect the critical component of our proposed architecture. The experimental results validate the effectiveness of the WDDL framework.

Associations between parental psychopathology and youth functional emotion regulation brain networks

Parental mental health is associated with children’s emotion regulation (ER) and risk for psychopathology. The relationship between parental psychopathology and children’s functional ER networks and whether connectivity patterns mediate the relationship between parent and youth psychopathology remains unexplored. Using resting-state functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study (N = 4202, mean age = 10.0) and a multilevel approach, we analyzed the relationship between self-reported parental psychopathology and their offsprings’ connectivity of four ER networks, as well as associations with self-reported youth psychopathology at a 3-year follow-up. Parental internalizing and total problems were associated with 1) higher connectivity between a subcortical-cortical integrative and ventrolateral prefrontal cortical (PFC) network, 2) lower connectivity between dorsolateral and ventrolateral PFC networks involved in cognitive aspects of ER, and 3) lower connectivity within a subcortical ER network (β = -0.05-0.04). Parental externalizing and total problems were associated with lower connectivity within the integrative network (βext = -0.05; βtot = -0.04). Mediation analyses yielded direct effects of parental to youth psychopathology, but no mediation effect of ER network connectivity. Overall, our results show that ER network connectivity in youth is related to parental psychopathology, yet do not explain intergenerational transmission of psychopathology.