Similar Connectivity Patterns Research Articles

The neuropeptidergic connectome of C. elegans

Efforts are ongoing to map synaptic wiring diagrams, or connectomes, to understand the neural basis of brain function. However, chemical synapses represent only one type of functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles in all nervous systems. By integrating single-cell anatomical and gene-expression datasets with biochemical analysis of receptor-ligand interactions, we have generated a draft connectome of neuropeptide signaling in the C.elegans nervous system. This network is characterized by high connection density, extended signaling cascades, autocrine foci, and a decentralized topology, with a large, highly interconnected core containing three constituent communities sharing similar patterns of input connectivity. Intriguingly, several key network hubs are little-studied neurons that appear specialized for peptidergic neuromodulation. We anticipate that the C.elegans neuropeptidergic connectome will serve as a prototype to understand how networks of neuromodulatory signaling are organized.

2022 개정 교육과정 고등학교 융합 선택 과목의 ‘핵심 아이디어’에 대한 의미연결망 분석

Objectives This study analyzes the semantic network of the ‘core ideas’ of the convergence elective course in the 2022 revised curriculum to identify the nature of convergence elective course and to extract the big ideas that are formed through learning about convergence elective course.
 Methods For this purpose, this study conducted morphological analysis on 120 ‘core ideas’ of 27 convergence elective course in the 2022 revised curriculum, and conducted structural equivalence analysis(CONvergence of iterated CORrelations) that group keywords with similar connection patterns into one cluster.
 Results As a result of analyzing the semantic network among the top 114 keywords extracted from the ‘core ideas’, 12 semantic clusters were extracted, which can be named as ‘social problem solving’, ‘convergent thinking ability and cooperative attitude’, ‘growth through experience’, ‘digital technology and media’, ‘ecological and environmental crisis’, ‘respect for diversity and self-reliance’, ‘understanding culture’, ‘personal growth and stable life’, ‘discovering the meaning of life’, ‘human life and arts and sports activities’, ‘value creation and intellectual property rights’, and ‘engineering design’.
 Conclusions Since the meaning networks of the ‘core idea’ of the convergence elective course were organized around competencies needed to solve real-world problems or themes that reflected real-world contexts, the convergence elective courses fulfill its original purpose. The meaning formed by the keywords in each cluster showed the possibility of a big idea.

The prefrontal cortex of the bottlenose dolphin (Tursiops truncatus Montagu, 1821): a tractography study and comparison with the human.

Cetaceans are well known for their remarkable cognitive abilities including self-recognition, sound imitation and decision making. In other mammals, the prefrontal cortex (PFC) takes a key role in such cognitive feats. In cetaceans, however, a PFC could up to now not be discerned based on its usual topography. Classical in vivo methods like tract tracing are legally not possible to perform in Cetacea, leaving diffusion-weighted imaging (DWI) as the most viable alternative. This is the first investigation focussed on the identification of the cetacean PFC homologue. In our study, we applied the constrained spherical deconvolution (CSD) algorithm on 3T DWI scans of three formalin-fixed brains of bottlenose dolphins (Tursiops truncatus) and compared the obtained results to human brains, using the same methodology. We first identified fibres related to the medio-dorsal thalamic nuclei (MD) and then seeded the obtained putative PFC in the dolphin as well as the known PFC in humans. Our results outlined the dolphin PFC in areas not previously studied, in the cranio-lateral, ectolateral and opercular gyri, and furthermore demonstrated a similar connectivity pattern between the human and dolphin PFC. The antero-lateral rotation of the PFC, like in other areas, might be the result of the telescoping process which occurred in these animals during evolution.

Exploring 3D community inconsistency in human chromosome contact networks

Abstract Researchers have developed chromosome capture methods such as Hi-C to better understand DNA’s 3D folding in nuclei. The Hi-C method captures contact frequencies between DNA segment pairs across the genome. When analyzing Hi-C data sets, it is common to group these pairs using standard bioinformatics methods (e.g. PCA). Other approaches handle Hi-C data as weighted networks, where connected node pairs represent DNA segments in 3D proximity. In this representation, one can leverage community detection techniques developed in complex network theory to group nodes into mesoscale communities containing nodes with similar connection patterns. While there are several successful attempts to analyze Hi-C data in this way, it is common to report and study the most typical community structure. But in reality, there are often several valid candidates. Therefore, depending on algorithm design, different community detection methods focusing on slightly different connectivity features may have differing views on the ideal node groupings. In fact, even the same community detection method may yield different results if using a stochastic algorithm. This ambiguity is fundamental to community detection and shared by most complex networks whenever interactions span all scales in the network. This is known as community inconsistency. This paper explores this inconsistency of 3D communities in Hi-C data for all human chromosomes. We base our analysis on two inconsistency metrics, one local and one global, and quantify the network scales where the community separation is most variable. For example, we find that TADs are less reliable than A/B compartments and that nodes with highly variable node-community memberships are associated with open chromatin. Overall, our study provides a helpful framework for data-driven researchers and increases awareness of some inherent challenges when clustering Hi-C data into 3D communities.

Whole-brain resting-state connectivity underlying impaired inhibitory control during early versus longer-term abstinence in cocaine addiction.

Lapses in inhibitory control have been linked to relapse in human drug addiction. Evidence suggests differences in inhibitory control depending on abstinence duration, but the underlying neural mechanisms remain unknown. We hypothesized that early abstinence (2-5 days) would be characterized by the strongest impairments of inhibitory control and most wide-spread deviations in resting-state functional connectivity of brain networks, while longer-term abstinence (>30 days) would be characterized by weaker impairments as compared to healthy controls. In this laboratory-based cross-sectional study, we compared individuals with Cocaine Use Disorder (iCUD) during early (cocaine urine-positive: N = 19, iCUD+; 32% female; mean age: 46.8 years) and longer-term abstinence (cocaine urine-negative: N = 29, iCUD-; 15% female; mean age: 46.6 years) to healthy controls (N = 33; 24% female; mean age: 40.9 years). We compared the groups on inhibitory control performance (Stop-Signal Task) and, using a whole-brain graph theory analysis (638 region parcellation) of functional magnetic resonance imaging (fMRI) data, we tested for group differences in resting-state brain function (local/global efficiency). We characterized how resting-state brain function was associated with inhibitory control performance within iCUD. Inhibitory control performance was worst in the early abstinence group, and intermediate in the longer-term abstinence group, as compared to the healthy control group (P < 0.01). More recent use of cocaine (CUD+ > CUD- > healthy controls) was characterized by decreased efficiency in fronto-temporal and subcortical networks (primarily in the salience, semantic, and basal ganglia networks) and increased efficiency in visual networks. Importantly, a similar functional connectivity pattern characterized impaired inhibitory control performance within iCUD (all brain analyses P < 0.05, FWE-corrected). Together, we demonstrated that a similar pattern of systematic and widespread deviations in resting-state brain efficiency, extending beyond the networks commonly investigated in human drug addiction, is linked to both abstinence duration and inhibitory control deficits in iCUD.

Two-in-one system and behavior-specific brain synchrony during goal-free cooperative creation: an analytical approach combining automated behavioral classification and the event-related generalized linear model.

In hyperscanning studies of natural social interactions, behavioral coding is usually necessary to extract brain synchronizations specific to a particular behavior. The more natural the task is, the heavier the coding effort is. We propose an analytical approach to resolve this dilemma, providing insights and avenues for future work in interactive social neuroscience. The objective is to solve the laborious coding problem for naturalistic hyperscanning by proposing a convenient analytical approach and to uncover brain synchronization mechanisms related to human cooperative behavior when the ultimate goal is highly free and creative. This functional near-infrared spectroscopy hyperscanning study challenged a cooperative goal-free creative game in which dyads can communicate freely without time constraints and developed an analytical approach that combines automated behavior classification (computer vision) with a generalized linear model (GLM) in an event-related manner. Thirty-nine dyads participated in this study. Conventional wavelet-transformed coherence (WTC) analysis showed that joint play induced robust between-brain synchronization (BBS) among the hub-like superior and middle temporal regions and the frontopolar and dorsomedial/dorsolateral prefrontal cortex (PFC) in the right hemisphere, in contrast to sparse within-brain synchronization (WBS). Contrarily, similar regions within a single brain showed strong WBS with similar connection patterns during independent play. These findings indicate a two-in-one system for performing creative problem-solving tasks. Further, WTC-GLM analysis combined with computer vision successfully extracted BBS, which was specific to the events when one of the participants raised his/her face to the other. This brain-to-brain synchrony between the right dorsolateral PFC and the right temporo-parietal junction suggests joint functioning of these areas when mentalization is necessary under situations with restricted social signals. Our proposed analytical approach combining computer vision and WTC-GLM can be applied to extract inter-brain synchrony associated with social behaviors of interest.

Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients.

Substantial clinical heterogeneity and comorbidity inherent amongst mental disorders limit the identification of neuroimaging biomarkers that can reliably track clinical symptoms. Strategies that enable generation of meaningful and replicable neurobiological markers at the individual level will push the field of neuropsychiatry forward in developing efficacious personalized treatment. The current study included 142 adult patients with a primary diagnosis of schizophrenia (SCZ), bipolar (BP), or attention deficit/hyperactivity disorder (ADHD), and 67 patient ratings across four behavioral measures. Using functional connectivity derived from a personalized fMRI approach, we identified several candidate imaging markers related to dimensional phenotypes across disorders, assessed the internal and external generalizability of these markers, and compared the probability of replicating findings across datasets using individual and group-averaged defined functional regions. We identified subject-specific connections related to three different clinical domains (attention deficit, appetite-energy, psychosis-positive) in a discovery dataset. Importantly, these connectivity biomarkers were robust and were reproduced in an independent validation dataset. For markers related to neurovegetative symptoms (attention deficit, appetite-energy symptoms), the brain connections involved showed similar connectivity patterns across the different diagnoses. However, psychosis-positive symptoms were associated with connections of varying strength across disorders. Finally, we found that markers for symptom domains were replicable for individually-specified connections, but not for group template-derived connections. Our personalized strategies allowed us to identify meaningful and generalizable imaging markers for symptom domains in patients who exhibit high levels of heterogeneity. These biomarkers may shed new light on the connectivity underpinnings of psychiatric symptoms and lead to personalized interventions.

The Angular Gyrus as a Hub for Modulation of Language-related Cortex by Distinct Prefrontal Executive Control Regions.

It has become clear in recent years that reading, while relying on domain-specific language processing regions, also involves regions that implement executive processes more broadly. Such executive control is generally considered to be implemented by prefrontal regions, which exert control via connectivity that allows them to modulate processing in target brain regions. The present study examined whether three previously identified and distinct executive control regions in the pFC [Wang, K., Banich, M. T., Reineberg, A. E., Leopold, D. R., Willcutt, E. G., Cutting, L. E., et al. Left posterior prefrontal regions support domain-general executive processes needed for both reading and math. Journal of Neuropsychology, 14, 467-495, 2020] show similar patterns of functional connectivity (FC) during a reading comprehension task as compared with a symbol identification condition. Our FC results in a sample of adolescents (n = 120) suggest all three regions commonly show associations with activity in "classic" left hemisphere reading areas, including the angular and supramarginal gyri, yet each exhibits differential connectivity as well. In particular, precentral regions show differential FC to parietal portions of the dorsal language stream, the inferior frontal junction shows differential FC to middle temporal regions of the right hemisphere and other regions involved in semantic processing, and portions of the inferior frontal gyrus show differential FC to an extensive set of right hemisphere prefrontal regions. These results suggest that prefrontal control over language-related regions occurs in a coordinated yet discrete manner.

Associated functional network connectivity between callous-unemotionality and cognitive and affective empathy

BackgroundLow empathy is one component of affective impairments defining the antisocial youth phenotype callous-unemotional (CU) traits. Research suggests CU traits may be negatively associated with neural networks that are positively associated with cognitive and affective empathy – specifically the default mode (DMN), frontoparietal (FPN), and salience (SAL) networks. Determining which functional network connections are shared between CU traits and empathy could elucidate the extent to which CU traits shares neural substrates with cognitive versus affective empathy. The present study tested whether CU traits and both cognitive and affective empathy share network connections within and between the DMN, FPN, and SAL. MethodsParticipants (n = 112, aged 13–17, 43 % female) completed resting-state functional magnetic resonance imaging and self-reports for CU traits and empathy as part of a Nathan-Kline Institute study. ResultsAnalyses revealed inverse associations with shared network connections between CU traits and both cognitive and affective empathy. Specifically, within-DMN connectivity negatively associated with CU traits, but positively associated with cognitive empathy; and between DMN-SAL connectivity positively associated with CU traits, but negatively associated with both cognitive and affective empathy. However, joint models revealed little variance explained by CU traits and empathy overlapped. LimitationsThe sample was cross-sectional collection with limited participants (n = 112) from the community that may not generalize to incarcerated adolescents. ConclusionsResults demonstrate CU traits inversely associated with similar connectivity patterns as cognitive and affective empathy though prediction among constructs did not significantly overlap. Further investigation of these connections can inform a mechanistic understanding of empathy impairments in CU traits.

Covariate-Assisted Community Detection in Multi-Layer Networks

ABSTRACT Communities in multi-layer networks consist of nodes with similar connectivity patterns across all layers. This article proposes a tensor-based community detection method in multi-layer networks, which leverages available node-wise covariates to improve community detection accuracy. This is motivated by the network homophily principle, which suggests that nodes with similar covariates tend to reside in the same community. To take advantage of the node-wise covariates, the proposed method augments the multi-layer network with an additional layer constructed from the node similarity matrix with proper scaling, and conducts a Tucker decomposition of the augmented multi-layer network, yielding the spectral embedding vector of each node for community detection. Asymptotic consistencies of the proposed method in terms of community detection are established, which are also supported by numerical experiments on various synthetic networks and two real-life multi-layer networks.

Altered effective connectivity among core brain networks in patients with bipolar disorder

BackgroundBipolar disorder (BD) is increasingly being regarded as a dysconnection syndrome. Functional integration among the three core brain networks – executive control network (ECN), salience network (SN), and default mode network (DMN) – is abnormal in patients with BD; however, the causal relationship among the three networks in BD is largely unknown. It is also unclear whether patients with BD in different mood states show distinct effective connectivity patterns during rest. MethodsResting-state fMRI data were collected from 65 patients with BD and 85 healthy controls. Spectral dynamic causal modeling was applied to investigate the effective connectivity difference of the three brain networks between all patients with BD and healthy controls and between patients who were in euthymic mood state (euthymic BD) and depressed mood state (depressed BD). ResultsCompared with healthy controls, all patients with BD showed altered effective connectivity within and between the ECN and SN and from these two networks to the DMN. Compared with patients with depressed BD, patients with euthymic BD showed increased excitatory effects within the ECN and decreased inhibitory effects from the SN to the ECN and DMN. ConclusionThese results further confirmed that patients with BD show abnormal functional integration within and among the three core brain networks, and exhibit similar and different effective connectivity patterns in different mood states. Abnormal effective connectivity has the potential to be a critical index for diagnosing BD and differentiating between BD patients with different mood states.

Perceptual coupling and decoupling of the default mode network during mind-wandering and reading.

While reading, our mind can wander to unrelated autobiographical information, creating a perceptually decoupled state detrimental to narrative comprehension. To understand how this mind-wandering state emerges, we asked whether retrieving autobiographical content necessitates functional disengagement from visual input. In Experiment 1, brain activity was recorded using functional magnetic resonance imaging (fMRI) in an experimental situation mimicking naturally occurring mind-wandering, allowing us to precisely delineate neural regions involved in memory and reading. Individuals read expository texts and ignored personally relevant autobiographical memories, as well as the opposite situation. Medial regions of the default mode network (DMN) were recruited during memory retrieval. In contrast, left temporal and lateral prefrontal regions of the DMN, as well as ventral visual cortex, were recruited when reading for comprehension. Experiment two used functional connectivity both at rest and during tasks to establish that (i) DMN regions linked to memory are more functionally decoupled from regions of ventral visual cortex than regions in the same network engaged when reading; and (ii) individuals with more self-generated mental contents and poorer comprehension, while reading in the lab, showed more decoupling between visually connected DMN sites important for reading and primary visual cortex. A similar pattern of connectivity was found in Experiment 1, with greater coupling between this DMN site and visual cortex when participants reported greater focus on reading in the face of conflict from autobiographical memory cues; moreover, the retrieval of personally relevant memories increased the decoupling of these sites. These converging data suggest we lose track of the narrative when our minds wander because generating autobiographical mental content relies on cortical regions within the DMN which are functionally decoupled from ventral visual regions engaged during reading.

A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort.

Connectivity‐based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes—the foundations of brain network fingerprinting—by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole‐brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort—MGH‐HCP. State‐of‐the‐art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain's structural network involving the decrease of intra‐hemispheric, increase of inter‐hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.

Hydrogen Bonding and Polymorphism of Amino Alcohol Salts with Quinaldinate: Structural Study.

Three amino alcohols, 3-amino-1-propanol (abbreviated as 3a1pOH), 2-amino-1-butanol (2a1bOH), and 2-amino-2-methyl-1-propanol (2a2m1pOH), were reacted with quinoline-2-carboxylic acid, known as quinaldinic acid. This combination yielded three salts, (3a1pOHH)quin (1, 3a1pOHH+ = protonated 3-amino-1-propanol, quin− = anion of quinaldinic acid), (2a1bOHH)quin (2, 2a1bOHH+ = protonated 2-amino-1-butanol), and (2a2m1pOHH)quin (3, 2a2m1pOHH+ = protonated 2-amino-2-methyl-1-propanol). The 2-amino-1-butanol and 2-amino-2-methyl-1-propanol systems produced two polymorphs each, labeled 2a/2b and 3a/3b, respectively. The compounds were characterized by X-ray structure analysis on single-crystal. The crystal structures of all consisted of protonated amino alcohols with NH3+ moiety and quinaldinate anions with carboxylate moiety. The used amino alcohols contained one OH and one NH2 functional group, both prone to participate in hydrogen bonding. Therefore, similar connectivity patterns were expected. This proved to be true to some extent as all structures contained the NH3+∙∙∙−OOC heterosynthon. Nevertheless, different hydrogen bonding and π∙∙∙π stacking interactions were observed, leading to distinct connectivity motifs. The largest difference in hydrogen bonding occurred between polymorphs 3a and 3b, as they had only one heterosynton in common.

“Antagonistic” cooperation of control regions in bilingual language production: An effective connectivity study

The dorso-lateral prefrontal cortex (DLPFC) and the anterior cingulate cortex (ACC) are two main regions that support bilingual language control during language production. Previous studies have suggested that the regions work together in language control processes. However, how they cooperate with each other during bilingual language production is far from clear. We addressed this issue by recruiting bimodal bilingual participants whose two languages could be separated at a neural level. Functional magnetic resonance imaging scans were acquired from the participants as they performed a dual-language production task and a spoken-production task. Dynamic causal modelling (DCM) was used to analyse the effective connectivity between the ACC and the DLPFC as well as the causal relationships among the control regions and language regions. We found that the ACC had positive effective connectivity to the DLPFC, left precentral gyrus and left superior temporal gyrus, while the DLPFC had negative effective connectivity to the other three regions. A similar pattern of effective connectivity between the control regions and language regions was also found in the spoken-production task. These results showed that the ACC may promote the production of the target language, while the DLPFC has an inhibitory effect in language production. The findings suggest an antagonistic relationship between the DLPFC and ACC that cooperatively ensures fluent language production in bilinguals.

MissSBM: An R Package for Handling Missing Values in the Stochastic Block Model

The stochastic block model is a popular probabilistic model for random graphs. It is commonly used to cluster network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting a stochastic block model to a partially observed network, it is important to consider the underlying process that generates the missing values, otherwise the inference may be biased. This paper presents missSBM, an R package that fits stochastic block models when the network is partially observed, i.e., the adjacency matrix contains not only 1s or 0s encoding the presence or absence of edges, but also NAs encoding the missing information between pairs of nodes. This package implements a set of algorithms to adjust the binary stochastic block model, possibly in the presence of external covariates, by performing variational inference suitable for several observation processes. Our implementation automatically explores different block numbers to select the most relevant model according to the integrated classification likelihood criterion. The integrated classification likelihood criterion can also help determine which observation process best fits a given dataset. Finally, missSBM can be used to perform imputation of missing entries in the adjacency matrix. We illustrate the package on a network dataset consisting of interactions between political blogs sampled during the 2007 French presidential election.

Link prediction for multilayer networks using interlayer structural information

A multilayer network is a useful representation for real-world complex systems in which multiple types of connections are formed between entities. Connections of the same type form a specific layer of the network. We propose a novel framework for predicting links in a target layer of a multilayer network by taking into account the interlayer structural information. The method depends on the intuitive assumption that two node pairs in the target layer tend to have similar connection patterns if these pairs of nodes are similar. Further, the prediction accuracy will be improved in the target layer if the structural information of the copies of the node pairs in relevant layers is employed. We demonstrate the effectiveness of the proposed method experimentally by applying it to both simulated and real-world multilayer networks.

Hippocampal-Medial Prefrontal Event Segmentation and Integration Contribute to Episodic Memory Formation.

How do we encode our continuous life experiences for later retrieval? Theories of event segmentation and integration suggest that the hippocampus binds separately represented events into an ordered narrative. Using a functional Magnetic Resonance Imaging (fMRI) movie watching-recall dataset, we quantified two types of neural similarities (i.e., "activation pattern" similarity and within-region voxel-based "connectivity pattern" similarity) between separate events during movie watching and related them to subsequent retrieval of events as well as retrieval of sequential order. We demonstrated that compared with forgotten events, successfully remembered events were associated with distinct "activation patterns" in the hippocampus and medial prefrontal cortex. In contrast, similar "connectivity pattern" between events were associated with memory formation and were also relevant for retaining events in the correct order. We applied the same approaches to an independent movie watching fMRI dataset as validation and highlighted again the role of hippocampal activation pattern and connectivity pattern in memory formation. We propose that distinct activation patterns represent neural segmentation of events, while similar connectivity patterns encode context information and, therefore, integrate events into a narrative. Our results provide novel evidence for the role of hippocampal-medial prefrontal event segmentation and integration in episodic memory formation of real-life experience.

Examining resting-state functional connectivity in key hubs of the default mode network in chronic low back pain.

Changes in brain connectivity have been observed within the default mode network (DMN) in chronic low back pain (CLBP), however the extent of these disruptions and how they may be related to CLBP requires further examination. While studies using seed-based analysis have found disrupted functional connectivity in the medial prefrontal cortex (mPFC), a major hub of the DMN, limited studies have investigated other equally important hubs, such as the posterior cingulate cortex (PCC) in CLBP. This preliminary study comprised 12 individuals with CLBP and 12 healthy controls who completed a resting-state functional magnetic resonance imaging (fMRI) scan. The mPFC and PCC were used as seeds to assess functional connectivity. Both groups displayed similar patterns of DMN connectivity, however group comparisons showed that CLBP group had reduced connectivity between the PCC and angular gyrus compared to healthy controls. An exploratory analysis examined whether the alterations observed in mPFC and PCC connectivity were related to pain catastrophizing in CLBP, but no significant associations were observed. These results may suggest alterations in the PCC are apparent in CLBP, however, the impact and functional role of these disruptions require further investigation.

Dissociable theta networks underlie the switch and mixing costs during task switching.

During task‐switching paradigms, both event‐related potentials and time‐frequency analyses show switch and mixing effects at frontal and parietal sites. Switch and mixing effects are associated with increased power in broad frontoparietal networks, typically stronger in the theta band (~4–8 Hz). However, it is not yet known whether mixing and switch costs rely upon common or distinct networks. In this study, we examine proactive and reactive control networks linked to task switching and mixing effects, and whether strength of connectivity in these networks is associated with behavioural outcomes. Participants (n = 197) completed a cued‐trials task‐switching paradigm with concurrent electroencephalography, after substantial task practice to establish strong cue‐stimulus–response representations. We used inter‐site phase clustering, a measure of functional connectivity across electrode sites, to establish cross‐site connectivity from a frontal and a parietal seed. Distinct theta networks were activated during proactive and reactive control periods. During the preparation interval, mixing effects were associated with connectivity from the frontal seed to parietal sites, and switch effects with connectivity from the parietal seed to occipital sites. Lateralised occipital connectivity was common to both switch and mixing effects. After target onset, frontal and parietal seeds showed a similar pattern of connectivity across trial types. These findings are consistent with distinct and common proactive control networks and common reactive networks in highly practised task‐switching performers.