Network hubs in the human brain 20140511 정미르

Introduction Brain: anatomically differentiated, functionally specialized (early times) ⇒ Specialization × fully account most functions Integrative process, dynamic interaction underpin cognitive processes What’s the neural substrate of integration? 1. Neural communication: synchronization, information flow 2. Specific brain regions & anatomical connections: complex & diverse response, ↑level hierarchy, confluence zones

Introduction Approach: ‘complex network’ Neural system: graphs, networks Network map, connectome: structural basis for dynamic interactions 1. unravel its architecture 2. explain how structural network topology shape & modulate function Connectome: diverse; modularity (specialization)+communication (integration) Network hub ensure integration: high degree connectivity, central placement

1. Methodological Aspects: Detection and Classification of Hubs in Brain Networks

Brain Network as Graphs Nodes- neuronal elements Edges- their interconnections Pair-wise couplings: connection matrix Arrangement: topology Graph theory offer data-driven measures to characterize network topology Identifies network elements having strong influence on the global network function

Hub Detection Network hub: nodes that make strong contributions to global network function Detected by… 1. Node degree (degree centrality): # of edges maintained by each node 2. Eigenvector centrality (pagerank centrality): favor nodes that connect to other highly central nodes 3. Closeness centrality: average distance btw a given node and the rest of the network 4. Betweenness centrality: # of short communication paths a node (or edge) participates in 5. Vulnerability: node deletion impact on global network communication 6. Dynamic importance: synchronization; graph metrics before & after deletion Rankings of nodes: highly correlated ⇒ aggregate rankings 4&5: identify central edges

Hub Detection Network community(=module): nodes more densely linked among each other than with nodes in other communities Participation coefficient: differentiates provincial hubs (single module) from connector hubs (multiple modules) Peripheral nodes: low-degree, connects within own module, ↓participation coefficient Hub nodes highly interconnected than by chance? Rich club: collectives of high-degree nodes and their interconnecting edges ⇒ facilitate mutual interactions Structural core: recursive pruning of nodes of increasing degree ⇒ resilient nodes (densely interconnected)

Structural Network Functional Network anatomical connectivity: Relatively stable (sec~min); plasticity (h~day) Edges: physical links; infrastructure for neuronal signaling & communication Derived from statistical descriptions of time series data fMRI ⇒ linear (Pearson) cross-correlations Time dependent, modulated by stimuli and task context, non- stationary fluctuations even at rest Edges: × represent anatomical connections Links many structurally unconnected node pairs, prone to transitivity ⇒ over-connection & high clustering Reflection of signaling & communication unfolded in structural network Define Hubs based on network’s community structure

2. Empirical Results: Candidate Hubs in the Structural and Functional Connectome

Structural Hubs Betweenness centrality: dorsal superior frontal(I), precuneus(II), occipital gyrus(III) Group-averaged: Precuneus(a), anterior(b) & posterior cingulate(c), superior frontal(d), dorsolateral prefrontal(e), insular(f), occipital(g), temporual gyrus(h) Other measures: structural vulnerability, node degree, multiple centrality metrics rankings (medial parietal, frontal & insular) Their Integrative & diversive properties ←central embedding within connection topology Functional properties are shaped by ‘connectional fingerprint’

Structural Hubs Also maintain ↑# of anatomical connections among each other Hub regions: more densely interconnected (wrt degree alone) ⇒ rich club (densely interconneted core) 1. Inter-hub communication robustness↑ 2. Promote efficient communication & integration across the brain Other aspects for classification of hubs as rich Structural network hubs & connections… 1. ↑wiring volume, ↑white matter (efficiency↑, cost↑) ⇒ Direct communication path, transmission delay↓, robustness↑ 2. metabolic active 3. complex cellular & microcircuit properties ⇒ Shaped by trade-offs btw wiring cost, spatial &metabolic constraint & efficiency Location of hubs: consistent High-degree regions: parietal, frontal, insular cortices Network hubs: universal feature of connectome organization

Functional Hubs Mearuse density (concentration) of local & global network functional connectivity Functional interactions: precuneus, (posterior) cingulate gyrus, inferior parietal, ventromedial frontal cortex ⇒ significant overlap w/ default mode network Recent approach: characterization of functional heterogeneity 1. Assess coactivation levels from various cognitive tasks 2. Participation of hubs in multiple functional domains 3. examination of functional paths layout within the network’s functional connectivity pattern :‘step-wise connectivity’ approach (unimodal→higher-order) Multimodal & Functional hubs: superior parietal, superior frontal cortex, cingulate gyrus, anterior insula (portion): part of resting-state network

Functional Hubs Other approach: examine participation across multiple functional networks & level of overlap btw different functional domain Primary regions (primary motor, visual & auditory): single or small # of functional networks Putative hub regions (medial superior frontal, anterior cingulate, precuneus/posterior cingulate gyrus): multiple functional networks Flexible network hubs: capacity to link & interact w/ diverse brain regions adaptively (+time & temporal variability) ☞frontoparietal, medial parietal & posterior cingulate Detection based on graph analysis: pair-wise statistical relationships (correlation coefficient btw recorded time series) Identification depends on 1. Network edge estimation methodology 2. Graph metrics expressing ‘functional centrality’ Spatial overlap w/ default mode network: central role ⇔ due to local interactions within them & functional degree biased by the network size

Individual Differences and Development of Hubs connectivity profile & functional coupling level variation: intelligence, performance in different cognitive domains, interhemispheric integration, personality traits Intelligence: medial parietal & prefrontal Cognitive control & intellectual performance: frontal Personality: medial parietal & cingulate Functional connectivity topology (high density): heritable Genetic influence ⇒structural integrity of long-range white matter tracts

Individual Differences and Development of Hubs Structural hubs emerge early, but functionally immature, confined to primary visual & motor Hub regions remain relatively stable; their interaction undergo developmental changes 1. ↑Functional connectivity among association areas 2. Spatially localized ⇒ globally distributed functional network through brain development Sex-related difference in hormone (eg. ↑LH) level affect white matter brain connectivity Genetic & environmental factors ⇒ individual variation on connectivity ⇒ cognition & behavior variation

Hubs in Brain Dysfunction Abnormal connectivity & functioning: cognitive impairment ←computational network studies Schizophrenia: frontal hub connectivity↓, disturbed rich club formation-‘long-standing dysconnectivity hypothesis’ Childhood-onset schizophrenia: disrupted modular architecture & disturbed connectivity in multimodal association cortex Autism: altered intra- & intermodular connectivity of densely connected limbic, temporal & frontal regions Alzheimer’s: medial parietal Frontotemporal dementia (FTD): frontal

Hubs in Brain Dysfunction Focal damage affect behavioral & cognitive functioning Lesions at functional connector hubs: disruption of modular organization Cognitive decline associated with white matter damage & ↓network integrity Damage to long-distance connections ⇒ network function & cognitive disruption

Hubs in Brain Dysfunction Reduced levels of consciousness related to disruption of hub connectivity Metabolic activity↓ in parietal precuneus & posterior cingulate hub Coma: random reorganization of functional hubs Vegetative & minimally conscious: persisting functional connectivity level

3. Conceptual Framework: Role of Hubs in Communication and Integration

Hubs and Network Communication Neural hubs derive their influence from their strong participation in dynamic interactions due to neuronal signaling Infer communication patterns focusing on layout of short paths & centrality of nodes (relative to these paths) Structural & functional hubs play a central role in global brain communication Hub regions may also render them potential neural ‘bottlenecks’ of information flow: set upper bounds for integration, chaining & serializing mental operations Mental disorders ← structural & functional connectivity disturbances Limitations⇒ × intrinsic to network models; reflect our ignorance & lack of data 1. capture only inter-regional projections, × include local circuits (transformation & recording) 2. × fully predict dynamic patterns of communication (local firing rates of neurons, Level of activity, external input, coherent phase relations, synaptic efficiency) some nodes may preferentially engage in neural communication; others rarely or never 3. assume nodes connect along most efficient paths, are accessible, dominant criterion Require detailed studies that track actual network paths of information flow

Hubs as Sources and Sinks Some cortical hub regions maintain an unequal balance of incoming and outgoing projections Receivers (sink): frontal & paracingulate cortex Emitters (source): cingulate, entorhineal, insular cortex ※ Consistent w/ inferred directionality of functional interactions

Hub Connections ‘edge-centric’ perspective: focus influence of edge on network organization Hub-hub & hub-(non-hub) edges: ↑proportion of long-distance connections 1. Short communication relays: transmission delay↓, interference & noise↓, faster synchronization ⇒ ‘Small world’; shortcuts × randomly placed; aggregate at hub nodes 2. efficient neural communication 3. robustness of inter-hub communication Strong hub connections (in white matter): structural modules & functional resting-state networks ⇐ vulnerability

Hubs and Cross-modal Integration Brain hubs & their connections: convergent structure for integration of information, forming putative substrate for functional ‘global workspace’ ☞cognitive architecture in which segregated functional systems can share & integrate information ←neuronal interactions Connective core hypothesis: interconnected hub regions topologically central offer an important substrate for cognitive integration: broadcasting, dynamic coupling, offering ‘arena for dynamic cooperation and competition’ Confluence (convergence) zones: hub regions across multiple functional domain overlapping area ↑involvement of hub nodes in intermodular connectivity

Hubs in Computational Models of Brain Dynamics Enable ↑level functional diversity & synchronization Central hub nodes engage on more variable or noisy dynamics ⇒ structural rewiring Highly connected hub nodes & connections dominate system’s dynamical organization : desynchronized ⇒ centrally synchronized dynamics Scale-free architecture: ↑functional diversity Hub nodes and edges lesions: most disruptive for network organization & functioning Network hubs: loci of high variability & plasticity (+maintaining the cortical synchronization, modularity structure, functional dynamics)

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