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Large-scale brain network
Large-scale brain network
Large-scale brain network
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Large-scale brain networks (also known as intrinsic brain networks) are collections of widespread brain regions showing functional connectivity by statistical analysis of the fMRI BOLD signal[1] or other recording methods such as EEG,[2] PET[3] and MEG.[4] An emerging paradigm in neuroscience is that cognitive tasks are performed not by individual brain regions working in isolation but by networks consisting of several discrete brain regions that are said to be "functionally connected". Functional connectivity networks may be found using algorithms such as cluster analysis, spatial independent component analysis (ICA), seed based, and others.[5] Synchronized brain regions may also be identified using long-range synchronization of the EEG, MEG, or other dynamic brain signals.[6]

The set of identified brain areas that are linked together in a large-scale network varies with cognitive function.[7] When the cognitive state is not explicit (i.e., the subject is at "rest"), the large-scale brain network is a resting state network (RSN). As a physical system with graph-like properties,[6] a large-scale brain network has both nodes and edges and cannot be identified simply by the co-activation of brain areas. In recent decades, the analysis of brain networks was made feasible by advances in imaging techniques as well as new tools from graph theory and dynamical systems.

Anatomical topographies of canonical large-scale networks

The Organization for Human Brain Mapping has created the Workgroup for HArmonized Taxonomy of NETworks (WHATNET) group to work towards a consensus regarding network nomenclature.[8] WHATNET conducted a survey in 2021 which showed a large degree of agreement about the name and topography of three networks: the "somato network", the "default network" and the "visual network", while other networks had less agreement. Several issues make the work of creating a common atlas for networks difficult: some of these issues are the variability of spatial and time scales, variability across individuals, and the dynamic nature of some networks.[9]

Some large-scale brain networks are identified by their function and provide a coherent framework for understanding cognition by offering a neural model of how different cognitive functions emerge when different sets of brain regions join together as self-organized coalitions. The number and composition of the coalitions will vary with the algorithm and parameters used to identify them.[10][11] In one model, there is only the default mode network and the task-positive network, but most current analyses show several networks, from a small handful to 17.[10] The most common and stable networks are enumerated below. The regions participating in a functional network may be dynamically reconfigured.[5][12]

Disruptions in activity in various networks have been implicated in neuropsychiatric disorders such as depression, Alzheimer's, autism spectrum disorder, schizophrenia, ADHD[13] and bipolar disorder.[14]

Commonly identified networks

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An example that identified 10 large-scale brain networks from resting state fMRI activity through independent component analysis[15]

Because brain networks can be identified at various different resolutions and with various different neurobiological properties, there is currently no universal atlas of brain networks that fits all circumstances.[16] Uddin, Yeo, and Spreng proposed in 2019[17] that the following six networks should be defined as core networks based on converging evidences from multiple studies[18][10][19] to facilitate communication between researchers.

Default mode (medial frontoparietal)

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Main article: Default mode network
  • The default mode network is active when an individual is awake and at rest. It preferentially activates when individuals focus on internally-oriented tasks such as daydreaming, envisioning the future, retrieving memories, and theory of mind. It is negatively correlated with brain systems that focus on external visual signals. It is the most widely researched network.[6][12][20][1][21][22][15][10][23][24][25]

Salience (midcingulo-insular)

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Main article: Salience network
  • The salience network consists of several structures, including the anterior (bilateral) insula, dorsal anterior cingulate cortex, and three subcortical structures which are the ventral striatum, substantia nigra/ventral tegmental region.[26][27] It plays the key role of monitoring the salience of external inputs and internal brain events.[1][6][12][21][15][10][23][25] Specifically, it aids in directing attention by identifying important biological and cognitive events.[27][24]
  • This network includes the ventral attention network, which primarily includes the temporoparietal junction and the ventral frontal cortex of the right hemisphere.[17][28] These areas respond when behaviorally relevant stimuli occur unexpectedly.[28] The ventral attention network is inhibited during focused attention in which top-down processing is being used, such as when visually searching for something. This response may prevent goal-driven attention from being distracted by non-relevant stimuli. It becomes active again when the target or relevant information about the target is found.[28][29]

Attention (dorsal frontoparietal)

[edit]
Main article: Dorsal attention network
  • This network is involved in the voluntary, top-down deployment of attention.[1][21][22][10][23][28][30][25] Within the dorsal attention network, the intraparietal sulcus and frontal eye fields influence the visual areas of the brain. These influencing factors allow for the orientation of attention.[31][28][24]

Central Executive (lateral frontoparietal)

[edit]
Main article: Frontoparietal network
  • This network initiates and modulates cognitive control and comprises 18 sub-regions of the brain.[32] There is a strong correlation between fluid intelligence and the involvement of the fronto-parietal network with other networks.[33][25]
  • Versions of this network have also been called the executive control network and the cognitive control network.[17]

Sensorimotor or somatomotor (pericentral)

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Main article: Sensorimotor network

Visual (occipital)

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Further information: Visual cortex
  • This network handles visual information processing.[34][25]

Other networks

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Different methods and data have identified several other brain networks, many of which greatly overlap or are subsets of more well-characterized core networks.[17]

See also

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References

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Revisions and contributorsEdit on WikipediaRead on Wikipedia

Large-scale brain network

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Large-scale brain networks, also known as macro-scale or intrinsic networks, are distributed ensembles of regions that exhibit synchronized, temporally correlated activity, forming the foundational functional of the human cerebral cortex. These networks, which consume a substantial portion of the metabolic resources, are largely task-independent and persist across various mental states, including , sleep, and goal-directed behaviors, reflecting the brain's intrinsic organization shaped by anatomical connectivity. Identified primarily through noninvasive neuroimaging techniques such as (rs-fMRI), these networks support domain-general processes underlying , , social interaction, and by dynamically integrating information across widespread regions. Seminal work has delineated the human cortex into seven canonical large-scale networks based on rs-fMRI data from healthy adults: the visual network (involved in visual processing), somatomotor network (supporting sensory-motor integration), dorsal attention network (facilitating top-down attentional control), ventral attention network (enabling stimulus-driven reorientation), limbic network (linked to affective and memory functions), frontoparietal control network (coordinating executive functions like and ), and default mode network (DMN) (active during introspection, self-referential thought, and ). These networks exhibit modular organization, with high within-network connectivity and sparser between-network interactions, often analyzed using to quantify properties like small-world , , and hub-like nodes in association cortices. Variations in network exist across studies, but efforts toward emphasize anatomical or functional descriptors to facilitate cross-study comparisons. The study of large-scale brain networks has revolutionized by shifting focus from localized brain regions to systems-level interactions, revealing how network dynamics underpin adaptive behaviors and . For instance, the DMN deactivates during externally oriented tasks to allow engagement of attention networks, while the detects motivationally relevant stimuli to switch between modes. In health, network integrity correlates with cognitive performance, such as higher global efficiency predicting better memory recall, and shows influenced by genetic factors. Disruptions in these networks are implicated in neuropsychiatric disorders: reduced DMN connectivity in contributes to memory deficits, inefficient wiring in affects thought processes, and altered frontoparietal dynamics in aging lead to executive decline. Advancing methodologies, including diffusion MRI for structural connectivity and for temporal dynamics, continue to refine our understanding of network development, plasticity, and individual differences. Cross-species comparisons highlight conserved architectures, such as homologous DMN-like networks in nonhuman , underscoring evolutionary relevance. Ongoing research integrates multimodal data to model network interactions, promising insights into therapeutic interventions like for restoring balance in diseased states.

Definition and Fundamentals

Definition and Scope

Large-scale brain networks are defined as distributed neural systems comprising interconnected brain regions that span multiple cortical lobes and extend across the entire brain, operating at a macroscopic scale from hundreds of square millimeters to whole-brain extents. These networks are primarily identified through neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), which reveal patterns of correlated activity among distant regions. A hallmark of their organization is modularity, where the brain is partitioned into semi-independent subnetworks that interact to mediate cognition, rather than functioning as isolated areas. Central characteristics of large-scale brain networks include intrinsic connectivity, evident in synchronized activity during rest states, and task-dependence, where network dynamics modulate based on cognitive demands. They exhibit , as demonstrated by graph-theoretic analyses, with densely connected hubs—such as regions in the association cortex like the —facilitating integration across subnetworks. These networks differ from local neural circuits, which rely on short-range synaptic connections within cortical columns or small regions on the millimeter scale, by emphasizing long-range axonal pathways that link disparate areas for coordinated processing. In contrast to global whole-brain activity patterns, which reflect diffuse , large-scale networks represent specialized, modular subsystems that support targeted functions. Large-scale brain networks emerged evolutionarily in mammalian , driven by rapid cortical expansion that untethered association areas from sensory hierarchies, enabling the formation of densely interconnected systems to underpin adaptive behaviors and complex .

Historical Development

The foundations of large-scale network research trace back to 19th-century anatomical studies that emphasized localized functions within the . In 1861, French physician identified a region in the left associated with through postmortem examinations of aphasic patients, challenging holistic views of function and establishing the localizationist . This was complemented by German neurologist Carl Wernicke's 1874 description of a posterior area linked to comprehension, further delineating discrete cortical zones for specific cognitive processes. These early observations laid the groundwork for understanding the as a collection of interconnected regions rather than a uniform entity. By the , the focus shifted toward connectionist models that highlighted inter-regional interactions. A seminal contribution came from Canadian psychologist Donald Hebb in his 1949 book The Organization of Behavior, where he proposed Hebb's rule: the idea that coincident neural activity strengthens synaptic connections, forming the basis for associative learning and distributed processing across neural assemblies. This principle influenced subsequent theories of neural circuitry, bridging individual neuron dynamics to emergent network behaviors. The modern era of large-scale brain networks emerged in the 1990s with advances in , particularly (fMRI). In 1995, Bharat Biswal and colleagues demonstrated intrinsic functional connectivity by showing synchronized low-frequency blood oxygen level-dependent (BOLD) signals in the during rest, without external stimuli, revealing task-independent network organization. This approach was expanded in 2001 by Marcus Raichle and team, who characterized the —a set of midline and parietal regions deactivated during tasks but active at rest—underscoring the brain's baseline operational mode. Key milestones in the 2010s refined network parcellation and topology. Brian Yeo et al. in 2011 used resting-state fMRI data from over 1,000 participants to divide the into seven and seventeen hierarchically organized intrinsic connectivity networks, providing a comprehensive atlas of large-scale functional architecture. Concurrently, Jonathan Power et al. in 2011 applied graph-theoretic analysis to resting-state data, identifying modular communities and hub regions that optimize information flow across the human . The integration of profoundly shaped this field in the 2000s, drawing on Duncan Watts and Steven Strogatz's 1998 model, which features high clustering and short path lengths for efficient communication. Early applications to brain data, such as Sophie Achard et al.'s 2006 analysis of fMRI , confirmed that human functional networks exhibit , enabling both specialized local processing and rapid global integration. This framework became foundational for quantifying network resilience and dynamics in subsequent studies.

Identification Methods

Functional Imaging Techniques

(fMRI) serves as the cornerstone for detecting large-scale brain networks through measurement of the blood-oxygen-level-dependent (BOLD) signal, which indirectly reflects neural activity via hemodynamic responses. This non-invasive technique captures synchronized fluctuations in brain activity across distant regions, enabling the mapping of functional connectivity without requiring external stimuli. Resting-state fMRI (rs-fMRI) examines intrinsic activity during wakeful rest, revealing large-scale networks through low-frequency BOLD fluctuations. Seed-based , a foundational approach, selects a (seed) and computes Pearson correlations between its time course and those of all other voxels to identify connected networks. (ICA) provides a data-driven alternative by decomposing the entire dataset into spatially independent components, each representing a potential network with associated time courses. Group-ICA extends this to multi-subject data, aggregating individual decompositions to extract consensus networks across populations, enhancing reliability for population-level inferences. In contrast, task-based fMRI employs experimental paradigms to probe network engagement during specific cognitive processes, using (GLM) contrasts to isolate activations. For instance, subtracting baseline from task conditions reveals co-activations within executive or networks, highlighting task-specific connectivity patterns. These designs, often block or event-related, allow delineation of how networks dynamically reorganize under cognitive demands. The BOLD signal's temporal resolution is constrained by the hemodynamic response, with typical repetition times (TR) of 2-3 seconds limiting direct capture of fast neural dynamics. Functional networks manifest through slow oscillations in the 0.01-0.1 Hz band, filtered during preprocessing to isolate relevant connectivity signals from physiological . Despite its strengths, fMRI faces limitations including susceptibility to motion artifacts, which can introduce spurious correlations even at sub-millimeter displacements. Preprocessing steps such as rigid-body realignment for motion correction and global signal regression are essential to mitigate these, though the latter remains debated for potentially introducing negative biases in connectivity estimates. Additionally, scanner variability across sites—arising from differences in , gradients, and protocols—can confound network , necessitating techniques for multi-site studies.

Structural and Multimodal Approaches

Structural approaches to mapping large-scale brain networks primarily rely on diffusion (dMRI) techniques to reconstruct white-matter pathways that connect distant regions. Diffusion tensor imaging (DTI), introduced as a method to quantify the directional diffusion of water molecules in tissue, models the diffusion process as an ellipsoid tensor, enabling the estimation of fiber orientation and integrity. In DTI, algorithms propagate streamlines along principal diffusion directions to delineate major white-matter tracts, such as the or cingulum, forming the structural backbone of networks like the default mode or executive control systems. A key metric in DTI is (FA), which measures the degree of anisotropy in diffusion, with higher FA values indicating more coherent fiber bundles and thus stronger structural connectivity; for instance, FA reductions in the superior longitudinal fasciculus have been linked to disrupted integrity in neurological disorders such as . To address limitations of DTI in regions with crossing fibers, high-angular resolution diffusion imaging (HARDI) acquires data at multiple diffusion directions, allowing higher-order models like imaging to resolve intravoxel heterogeneity without assuming a single tensor. HARDI-based improves accuracy in complex areas, such as the where fibers from different networks intersect, by estimating orientation distribution functions (ODFs) that capture multiple populations within a . This approach has revealed more detailed structural connectomes, including enhanced delineation of thalamo-cortical pathways in sensory-motor networks. Another structural method, structural covariance analysis, infers network organization from inter-individual correlations in gray-matter morphology, often using voxel-based morphometry (VBM) to segment and normalize brain volumes. VBM computes regional gray-matter density or volume, then applies correlation matrices across subjects to identify co-varying regions, revealing networks such as cortico-limbic structures associated with emotional processing; for example, covariance between the and highlights structural substrates of the . This technique complements by focusing on gray-matter hubs rather than direct axonal connections. Multimodal approaches integrate structural data with functional or metabolic to provide a more comprehensive view of networks, combining the anatomical specificity of dMRI with temporal or physiological insights from other modalities. Fusion of DTI or HARDI with functional MRI (fMRI), for instance, correlates white-matter tracts with resting-state connectivity patterns to validate structural-functional relationships, such as aligning the structural uncinate fasciculus with functional limbic network activity. Similarly, combining dMRI with (EEG) or (MEG) leverages the high temporal resolution of electrophysiological signals to map dynamic network propagation along structural pathways, enhancing spatiotemporal characterization of executive control dynamics. (PET) integration adds metabolic correlates, where glucose uptake patterns from PET are overlaid with to link structural connectivity to energy demands in networks like the . Recent advances as of 2025 include the application of and algorithms to enhance accuracy, automate network parcellation in fMRI data, and denoise multimodal datasets, as well as the use of ultra-high-field (7T) MRI for improved in mapping. Despite these advances, structural and multimodal methods face significant challenges. , particularly in DTI, struggles with crossing fibers, leading to erroneous terminations or false positives with high false positive rates, such as 64% of recovered bundles being invalid, particularly in complex regions, as shown in benchmark studies using data. HARDI mitigates this but increases acquisition time and computational demands. Structural covariance provides indirect inferences, susceptible to factors like age or , without confirming direct anatomical links. Multimodal fusion encounters alignment issues across modalities due to differing s and physiological , complicating the interpretation of structure-function . Ongoing efforts focus on advanced modeling and validation against histological data to refine these techniques.

Core Brain Networks

Default Mode Network

The default mode network (DMN) is a prominent large-scale brain network characterized by its consistent activity during periods of introspective thought and rest, comprising key bilateral and symmetric cortical regions. These core hubs include the medial prefrontal cortex (mPFC), (PCC) and , and in the . The mPFC, often subdivided into ventral and dorsal portions, supports self-referential processing, while the PCC/ integrates and spatial navigation, and the facilitates semantic integration and . This anatomical configuration enables the DMN to function as an intrinsic system, maintaining coherence even in the absence of external stimuli. The DMN exhibits a distinct activation profile, becoming prominently engaged during internally directed cognition such as mind-wandering, autobiographical memory recall, and self-referential thinking, while showing reliable deactivation during tasks demanding focused attention on external demands. For instance, during rest or passive states, DMN regions demonstrate increased blood-oxygen-level-dependent (BOLD) signals, reflecting heightened metabolic activity that supports spontaneous thought generation. In contrast, externally oriented tasks like visual attention or working memory suppress DMN activity, allowing resources to shift toward task-positive networks. This task-induced deactivation pattern, first systematically identified in positron emission tomography studies, underscores the DMN's role as a baseline state interrupted by goal-directed behavior. Functionally, the DMN displays robust intrinsic connectivity, with high within-network correlations typically ranging from 0.2 to 0.4 in resting-state (rs-fMRI) data, particularly between the mPFC and PCC. These positive correlations reflect synchronized low-frequency fluctuations that maintain network integrity. Concurrently, the DMN shows negative correlations, or anti-correlations, with executive control networks, such as the , which become evident during cognitive demands and highlight competitive dynamics between internal and external processing modes. The network further organizes into subnetworks, including an anterior medial component centered in the mPFC, which is preferentially involved in and present-self reflection, and a posterior medial component encompassing the PCC and , which supports memory retrieval and future-oriented simulation.

Salience Network

The , also known as the midcingulo-insular network, is a large-scale system primarily anchored in the anterior insula and the dorsal (dACC). These cortical hubs form the core of the network, with extensions to subcortical structures including the ventral and , which contribute to its role in processing motivationally and emotionally relevant information. This network functions to detect and integrate salient stimuli, encompassing both emotional and sensory inputs that are behaviorally relevant, thereby facilitating the allocation of neural resources to significant events. A key role involves dynamic switching between the and the executive control network, enabling the brain to transition from internally directed thought to externally oriented task performance when salient cues arise. The exhibits strong connectivity to autonomic regions, such as those involved in visceral and interoceptive processing, allowing it to regulate physiological responses to salient stimuli and maintain . Its architecture features a low modular index and high participation coefficient, reflecting extensive between-network communication that positions it as a connector hub facilitating integration across systems. Variability in the includes a right-lateralized dominance, particularly in the right anterior insula, which supports shifting and rapid response to salient environmental changes.

Executive Control Network

The executive control network, also known as the central executive network or lateral frontoparietal network, is a core task-positive brain system that facilitates higher-order cognitive processes. Its primary regions include the (DLPFC), posterior parietal cortex (PPC), and inferior frontal junction (IFJ), which form interconnected hubs for integrating and directing goal-oriented activity. These areas exhibit strong functional connectivity during demanding tasks, enabling the network to support maintenance, under uncertainty, and to suppress irrelevant responses. As a task-positive network, the executive control network activates robustly during externally focused , contrasting with the through anti-correlated activity patterns that facilitate shifts between and task engagement. It plays a pivotal role in , where the DLPFC and PPC encode and manipulate task-relevant information, while the IFJ contributes to rapid task switching and . Seminal work has identified its involvement in accumulating evidence for choices across timescales, underscoring its importance in flexible behavior. The network can be subdivided into the central executive fronto-parietal component, emphasizing transient and adaptive processes, versus broader control systems like the cingulo-opercular network for sustained task sets. Hubs within the fronto-parietal regions demonstrate high degree centrality, reflecting their role as connectors in communication and their sensitivity to task complexity. Activation in these areas scales with , showing linear increases in BOLD signal during escalating demands. Furthermore, connectivity strength within the network correlates positively with scores, with reported associations ranging from moderate (r = 0.53) to strong (r = 0.91), linking it to individual differences in abstract reasoning.

Sensory and Motor Networks

Dorsal Attention Network

The (DAN), also known as the frontoparietal attention network, is a large-scale system specialized for voluntary, top-down control of , particularly in visuospatial domains. It enables the endogenous allocation of attentional resources to behaviorally relevant stimuli based on internal goals or intentions, facilitating processes such as and spatial orienting. This network operates in contrast to bottom-up, stimulus-driven mechanisms, allowing for flexible redirection of focus without reliance on salient external cues. Key nodes of the DAN include the (FEF) in the , the (IPS) in the parietal cortex, and the (SPL). The FEF is involved in the planning and execution of saccadic eye movements, integrating attentional signals with motor commands for gaze shifts. The IPS serves as a hub for spatial representation and attentional selection, modulating activity in visual areas to enhance processing of attended locations. The SPL contributes to integrating sensory information for spatial awareness and goal-directed attention. These regions form a bilateral network, though functional activation often shows a right-hemisphere bias during spatial tasks, such as unilateral neglect recovery or asymmetric orienting. Functionally, the DAN supports endogenous by generating top-down signals that bias toward task-relevant features. For instance, during goal-directed , it enhances detection of targets defined by conjunctions of features (e.g., color and shape) by suppressing irrelevant distractors. This is evident in studies where DAN activation correlates with improved performance in cueing paradigms, where predictive cues direct to specific spatial locations. The network's role in voluntary orienting is distinct from reflexive reorienting, which involves transient interactions with the ventral attention network for salient, unexpected events. In terms of connectivity, the DAN exhibits strong top-down projections from frontal and parietal nodes to early sensory cortices, such as V1 and V4, to amplify neural responses at attended locations. These projections enable rapid modulation of perceptual sensitivity, with feedback loops allowing iterative refinement of attentional focus. Notably, the DAN displays relatively low within-network modularity compared to other systems like the default mode network, promoting high flexibility and adaptability during dynamic task demands. Structural connectivity, revealed by diffusion tensor imaging, underscores these pathways via white matter tracts like the superior longitudinal fasciculus linking FEF and IPS.

Sensorimotor Network

The sensorimotor network, also known as the somatomotor network, constitutes a core large-scale system centered around the pericentral region, integrating sensory inputs from the body with motor outputs to facilitate coordinated movement. This network encompasses the primary sensorimotor cortices, including the (, ) and (, Brodmann areas 3, 1, and 2), which form the core hub along the . Additional key regions include the (SMA) in the medial frontal cortex for movement initiation and sequencing, as well as subcortical components such as loops involving the dorsolateral striatum ( in ), , and , which modulate motor execution through parallel cortico-basal ganglia-thalamo-cortical pathways. A defining property of the sensorimotor network is its somatotopic organization, where body parts are represented in a mirroring their physical layout, with adjacent cortical areas corresponding to neighboring body regions such as the hand, foot, and . This organization is evident in the primary sensorimotor cortices and extends into subcortical loops, enabling precise spatial coding of sensory and motor signals. The network exhibits high structural connectivity, primarily through the corticospinal tracts, which originate from layer V pyramidal neurons in the sensorimotor cortex and descend via the to synapse on spinal motoneurons and , supporting efficient transmission of motor commands to the periphery. Activation within the sensorimotor network synchronizes during movement planning and execution, with functional connectivity strengthening across its regions to coordinate sensory feedback and motor commands. (EEG) recordings reveal prominent beta-band oscillations (approximately 15-30 Hz) in the sensorimotor cortex, which desynchronize (event-related beta desynchronization) prior to and during voluntary movements, reflecting reduced inhibition and facilitation of motor output. Post-movement, these oscillations rebound (post-movement beta rebound), aiding in the stabilization of motor states. The network's integration occurs through closed-loop feedback mechanisms that incorporate proprioceptive signals from muscle spindles and joint receptors to monitor and correct motor errors in real-time. These loops involve reciprocal connections between the sensorimotor cortex and , where sensory afferents are modulated via presynaptic inhibition in spinal , allowing dynamic adjustment of motor plans based on ongoing sensory input. Such integration ensures during tasks requiring precision, with the sensorimotor network occasionally interfacing with networks for enhanced orienting during complex actions.

Visual Network

The visual network constitutes a large-scale, occipital-dominant brain circuit dedicated to processing visual information through hierarchical feature extraction, transforming raw retinal input into perceptually meaningful representations. This network encompasses core areas including the primary visual cortex (V1), which detects basic edges and orientations; extrastriate regions such as V2, V3, V4, and V5/MT, which integrate contour, texture, and motion features; and higher-order zones like the lateral occipital complex (LOC), which supports object form perception.00774-X.pdf) The hierarchy progresses from low-level retinotopic processing in V1 to abstract invariant representations in the LOC, enabling efficient encoding of complex scenes.00774-X.pdf) The network's organization features two primary processing streams diverging from early visual cortices: the ventral stream, routed through V4 toward the for ("what" pathway), and the dorsal stream, directed via MT to parietal regions for motion and ("where/how" pathway). This dual-stream architecture, evidenced by differential deficits following targeted lesions in , facilitates parallel handling of identification and action-oriented tasks. Retinotopic mapping underlies this organization, mapping the contralateral onto cortical surfaces with systematic representation of eccentricity and polar angle, and amplified for central (foveal) vision to match behavioral acuity.00774-X.pdf) Connectivity within the visual network begins with feedforward projections from the (LGN) of the to V1, relaying segregated magnocellular (motion/luminance) and parvocellular (color/form) pathways from the . Recurrent loops from higher areas, such as MT and LOC back to V1 and V2, enable top-down modulation that sharpens feature selectivity, particularly during attentional tasks. The visual network's activity is further influenced by interactions with the , enhancing processing of behaviorally relevant stimuli. Specialized subnetworks within the handle distinct attributes: V4 forms a color-processing hub, with neurons tuned to hue and saturation for achieving ; the LOC specializes in shape and form invariance, robust to viewpoint changes; and MT/V5 constitutes a motion subnetwork, computing direction and speed for dynamic scene analysis. The network demonstrates adaptive plasticity, particularly in response to defects, where surviving cortical regions remap receptive fields to compensate for lost input, as observed in adult following focal lesions.

Network Interactions and Dynamics

Functional Connectivity Patterns

Functional connectivity patterns in large-scale brain networks refer to the stable, time-averaged correlations in BOLD signal fluctuations observed during resting-state fMRI, revealing both homogeneous connections within networks and distinct inter-network relationships among core and sensory systems. Within-network homogeneity is particularly pronounced in the (DMN), where correlations between regions reflect tight coupling among hubs like the and medial prefrontal cortex. This internal coherence extends to other networks, such as the visual and sensorimotor systems, and is quantified through modular structures identified by community detection algorithms like on matrices, which delineate distinct modules with higher intra-module than inter-module connectivity. Between-network patterns exhibit a mix of positive correlations and anti-correlations, facilitating segregated yet integrated processing across the brain. The triple-network model highlights key interactions among the DMN, (SN), and executive control network (ECN), where the SN acts as a switch modulating anti-correlations between the task-negative DMN and task-positive ECN to support adaptive . Additionally, rich-club characterizes the , with high-degree hub regions (e.g., in prefrontal and parietal cortices) forming densely interconnected cores that enhance global integration while maintaining network segregation. Graph-theoretic metrics applied to these connectomes underscore efficient and local clustering. Global , measuring the average inverse shortest path length across all node pairs, remains high in healthy adult brains (typically 0.4-0.6 in normalized units), indicating short communication paths akin to small-world . The , which quantifies the density of triangles around each node, also shows elevated values (around 0.5-0.6), supporting robust local processing within modules. These metrics reveal a balance between segregation and integration in static patterns. Variability in functional connectivity emerges across the lifespan, with age-related declines particularly evident in core networks. In older adults (over 60 years), within-DMN connectivity decreases nonlinearly, particularly in antero-posterior correlations compared to young adults, contributing to reduced network efficiency. Within-network connectivity in primary sensory networks like the shows no significant reductions, though inter-network connections may strengthen compensatorily in some cases.

Dynamic Reconfigurations

Large-scale brain networks exhibit dynamic reconfigurations, characterized by time-varying functional connectivity (FC) that reflects the 's ability to adapt to changing cognitive demands or internal states. In resting-state (rs-fMRI), sliding-window analysis is a primary method to capture these fluctuations, where short temporal windows (typically 30-60 seconds) are used to compute matrices across brain regions, revealing recurring patterns of connectivity changes. These analyses have identified quasi-periodic patterns (QPPs) in rs-fMRI signals, which are reliable spatiotemporal motifs recurring every 20-60 seconds, involving coordinated activity between networks such as the (DMN) and task-positive networks. QPPs contribute significantly to overall FC estimates, accounting for up to 50% of the variance in static connectivity measures, and highlight the 's intrinsic oscillatory dynamics even in the absence of tasks. State transitions in brain network configurations can be modeled using Markov processes, which describe probabilistic switches between discrete connectivity states over time. Hidden Markov models (HMMs), in particular, infer latent states from observed rs-fMRI data by estimating transition probabilities and state-specific FC patterns, often revealing 4-8 recurring states with dwell times of 10-30 seconds. During cognitive tasks, these models show increased prevalence of states featuring strengthened anti-correlations, particularly between the DMN and executive control or salience networks, which enhances task-specific network segregation and supports focused attention. For instance, anti-correlations that are modest at rest can increase significantly in magnitude during demanding tasks, facilitating the suppression of task-irrelevant activity. Factors such as and profoundly influence these reconfigurations, modulating the frequency and stability of network states. High arousal levels promote shifts toward states with greater between-network integration, enhancing global communication across sensory, attentional, and executive systems, while low arousal or leads to increased within-network stability but reduced flexibility. Neuromodulators like further regulate dynamic FC; elevated dopamine signaling, as induced by pharmacological agents, stabilizes specific states associated with motor and attentional networks, reducing variability in transitions and supporting sustained cognitive performance. HMMs have been instrumental in identifying dynamic communities—transient modules of co-activating regions—by parsing rs-fMRI into hidden states that represent adaptive groupings beyond static parcellations.

Functional Roles

Role in Cognition and Behavior

Large-scale brain networks play pivotal roles in supporting various cognitive processes, with the (DMN) being particularly essential for retrieval. The DMN facilitates the construction and recall of personally relevant experiences by integrating internal narratives, with key nodes such as the aiding autobiographical recall and the medial prefrontal cortex regulating encoding and retrieval processes. Experimental evidence from shows that DMN activation increases during tasks, enabling relational binding of memory elements from an egocentric perspective. In parallel, the executive control network underpins problem-solving and interference resolution, as demonstrated in tasks like the Stroop test. Meta-analyses of neuroimaging studies reveal consistent activation in this network's core regions, including the dorsolateral prefrontal cortex and anterior cingulate cortex, during inhibitory control demands where conflicting stimuli must be resolved to guide goal-directed behavior. This network's engagement allows for flexible cognitive adjustments, such as suppressing automatic responses to prioritize task-relevant information. On the behavioral front, the drives rapid responses to threats by detecting and prioritizing salient environmental cues. Anchored in the anterior insula and dorsal anterior cingulate, this network signals homeostatic demands, including potential dangers, to initiate adaptive physiological and motor reactions. Meanwhile, attention networks, particularly the , mediate trade-offs between () and exploitation in scenarios. Functional MRI studies indicate that exploration activates control and attention regions for scanning novel options, whereas exploitation recruits default network areas for leveraging known rewards. The integration of these networks enables coordinated handling of complex demands, such as multitasking, where efficient switching between states preserves integrity. Neuroimaging reveals that successful multitasking involves dynamic reconfiguration, with disruptions in older adults linked to persistent connectivity to interrupting stimuli rather than rapid disengagement. Within frameworks, large-scale networks update internal priors through hierarchical inference, minimizing prediction errors via bottom-up sensory signals and top-down expectations across cortical layers. Individual differences in network efficiency further modulate these functions, with higher associated with enhanced in parieto-frontal regions, allowing easier access to optimal neural states. Similarly, creative ability correlates with stronger functional connectivity within a high-creative network spanning default, salience, and executive systems, predicting performance across validation datasets (r ≈ 0.13–0.35).

Integration with Neural Circuits

Large-scale brain networks emerge from the intricate interplay between local neural circuits and global connectivity patterns, particularly through that sustain rhythmic activity across distributed regions. These loops involve reciprocal projections between the and cortex, where thalamic relay cells excite cortical pyramidal neurons, and cortical feedback modulates activity to generate synchronized oscillations. In detailed computational models incorporating millions of spiking neurons and billions of synapses, such loops produce self-sustained rhythms including delta (1–3 Hz), alpha (~10 Hz), beta (~20 Hz), and gamma (40–50 Hz) bands, mimicking patterns observed in mammalian brains. This micro-macro linkage ensures that local circuit dynamics propagate to support network-level coherence, with propagating waves traveling at approximately 0.1 m/s. Central to this integration is the excitatory-inhibitory (E/I) balance within neocortical hubs, which are densely connected nodes like those in the that facilitate information routing across networks. In these hubs, excitatory inputs from pyramidal neurons are precisely counterbalanced by inhibition from , such as parvalbumin-positive basket cells, to maintain network stability and prevent hyperexcitability. This balance regulates signal gain and oscillatory synchronization; for instance, disruptions in inhibitory function lead to altered long-range connectivity, as seen in conditions affecting cognitive . Minicolumnar structures in hubs exemplify this, where core excitatory populations are flanked by inhibitory surrounds, enabling efficient local computation that scales to global network function. Oscillatory mechanisms further bridge local circuits to network communication, with gamma-band oscillations (~40 Hz) promoting local binding of neuronal ensembles within regions. Generated by feedback loops between pyramidal cells and fast-spiking interneurons, gamma rhythms cluster firing to represent individual items or features, as evidenced by synchronous place cell activity in the hippocampus. In contrast, theta-band oscillations (~4–8 Hz) facilitate inter-network coordination, where phase precession and high coherence (e.g., >0.8 between hippocampus and prefrontal cortex) align activity across distant areas to sequence multi-item information. Theta-gamma cross-frequency coupling thus enables hierarchical processing, with theta modulating gamma bursts for effective long-range transmission. Synaptic provides the adaptive foundation for this integration, where Hebbian learning rules at the cellular level—manifested as (LTP) and long-term depression (LTD)—scale to rewiring of large-scale networks through experience-dependent changes. LTP strengthens active synapses by increasing insertion, while LTD weakens them, often heterosynaptically to compensate for potentiated connections and maintain overall stability. These mechanisms, combined with homeostatic plasticity, adjust synaptic strengths across circuits; for example, in rodents induces multi-synapse bouton formation on dendrites, enhancing local excitation while balancing adjacent synapses to support network reorganization. Such plasticity ensures that local circuit modifications propagate to alter functional connectivity patterns in response to environmental demands. Computational models, particularly neural mass models, simulate how these circuit dynamics give rise to emergent network properties by aggregating neuronal populations into macroscopic variables. These models, often based on microcircuits with excitatory and inhibitory subpopulations, use differential equations to capture nonlinear interactions and connectivity matrices that drive . For instance, perturbation analyses reveal transitions between oscillatory states (e.g., from fixed points to limit cycles) influenced by synaptic weights, linking local E/I balance to global rhythms like those in . Such frameworks enable predictions of large-scale dynamics from underlying circuit parameters, providing mechanistic insights into network emergence.

Clinical and Research Applications

Associations with Neurological Disorders

In , disruptions in large-scale brain networks, particularly the (DMN), are prominent and progression-linked. Early stages often exhibit hyperconnectivity within the DMN, which correlates with initial amyloid-beta deposition in network hubs such as the posterior cingulate and , potentially reflecting compensatory mechanisms before cognitive decline manifests. As the disease advances, this shifts to hypoconnectivity in the DMN, alongside reduced interactions between the DMN and frontoparietal networks, which aligns with pathology spread and amyloid accumulation preferentially targeting DMN regions. These network alterations contribute to memory impairments by impairing the integration of internally directed . Schizophrenia is characterized by decoupling between the salience network (SN) and executive control networks, leading to aberrant attribution of salience to irrelevant stimuli. Functional connectivity reductions within the fronto-parietal network, a key executive system, are consistently observed, correlating with deficits in cognitive control and working memory. This decoupling extends to weakened SN interactions with the central executive network, as evidenced by diminished anti-correlations during task performance, which underlies disorganized thought and perceptual anomalies. Intra-network connectivity in the SN is also reduced, particularly involving the insula, further exacerbating affective and cognitive dysregulations. In attention-deficit/hyperactivity disorder (ADHD), deficits in the (DAN) manifest as hypoactivation in regions during attention-demanding tasks, contributing to impaired sustained focus and orienting. Additionally, increased intra-network variability within the DAN and between the DAN and is noted in resting-state analyses, reflecting unstable attentional states and heightened distractibility. These dynamic fluctuations in DAN connectivity correlate with behavioral measures like reaction time variability, highlighting network instability as a core pathophysiological feature. Major depressive disorder involves DMN overactivity, particularly heightened functional connectivity between the DMN and subgenual , which is strongly linked to rumination and self-referential negative bias. Concurrently, the shows hypoactivation and reduced within-network connectivity, especially in the insula, impairing the detection of relevant emotional cues and contributing to and motivational deficits. This imbalance between DMN dominance and SN underengagement sustains depressive symptoms by prioritizing maladaptive over external engagement.

Advances in Neuroimaging and Modeling

Recent advances in neuroimaging have leveraged ultra-high-field magnetic resonance imaging (MRI) at 7 Tesla (7T) to achieve sub-millimeter spatial resolution, enabling finer parcellation of large-scale brain networks by reducing partial volume effects and enhancing functional contrast-to-noise ratios. This technique has facilitated the delineation of individual-specific subregions within networks such as the auditory cortex, revealing heterogeneous functional connectivity patterns that are obscured at lower fields like 3T. In parallel, optogenetics in animal models has provided causal insights into network dynamics by selectively activating or silencing genetically targeted neurons, demonstrating, for instance, how perturbations in primate anterior insular cortex disrupt default mode network (DMN) synchronization and decoupling. Computational modeling has advanced the inference of directed interactions within networks through (DCM), a Bayesian framework that estimates effective connectivity by integrating neural dynamics with hemodynamic responses in fMRI . Originally proposed by Friston and colleagues, DCM employs bilinear approximations of neural states to model context-dependent influences between regions, outperforming undirected measures in identifying causal hierarchies in tasks like and processing. Complementing this, approaches, particularly deep neural networks, have improved predictions of network states from functional connectivity matrices; for example, graph convolutional networks capture topological features to forecast cognitive performance with accuracies exceeding 70% in large cohorts. Therapeutic applications have emerged from these techniques, with real-time fMRI neurofeedback enabling voluntary modulation of the DMN to alleviate symptoms in disorders like , where training to downregulate posterior cingulate activity reduces hyperconnectivity and auditory hallucinations. Similarly, (TMS) targeted at network hubs, such as the , induces frequency-specific changes in intrinsic connectivity, enhancing anticorrelations between the DMN and executive networks to support cognitive control. Looking ahead, updates to the (HCP) since 2020 have released multi-modal datasets with enhanced diffusion MRI resolution, including the HCP-Young Adult 2025 Release (as of August 2025) featuring updated processing on 3T and 7T imaging data for improved spatial and temporal , supporting probabilistic whole-brain atlases that integrate structural and functional parcellations across subjects for individualized mapping. AI-driven methods, including network classification algorithms, continue to refine connectotype profiling; as of 2025, precision neurodiversity approaches use topological deviation indices to quantify individual network reorganization for personalized interventions in and clinical populations, while advances in network models simulate neuropathological mechanisms and evaluate therapeutic efficacy.

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